project engineer

planning engineer

devops engineer

ai engineer

infrastructure engineer

data platform engineer

Times World Information Technology

<p></p><div><p>We are looking for a Senior Data &amp; AI Platform Engineer with strong expertise in data architecture, database design, and scalable system development. The ideal candidate should have a deep understanding of data engineering concepts, modern data platforms, and the ability to design robust, scalable back-end systems and data pipelines.</p><p></p>
<div><h4 class="jobSectionHeader"><b>Key responsibilities:</b></h4>
<ul><li>Design and develop scalable back-end systems</li><li>
Define and implement data architecture and database design strategies</li><li>
Build and optimize data ingestion and transformation pipelines
</li><li>Develop and maintain unified data models across multiple systems
</li><li>Design end-to-end data flows and integration strategies
</li><li>Collaborate with cross-functional teams to ensure efficient data handling and processing
</li><li>Ensure system scalability, performance, and reliability</li></ul><h4 class="jobSectionHeader"><b>
Machine Learning &amp; AI Integration</b></h4><ul><li>
Work closely with the AI/ML team to understand data requirements for model training, feature engineering, and pipeline development
</li><li>Prepare, validate, and deliver clean, structured data to support AI/ML systems and intelligent workflows
</li><li>Consume and integrate outputs from ML models into data pipelines and backend systems
</li><li>Maintain awareness of ML concepts to effectively collaborate with AI/analytics teams</li></ul><h4 class="jobSectionHeader"><b>
Core Technical Skills</b></h4><ul><li>
Back-end &amp; System Design</li><li>
Expertise in system scalability and performance optimization
</li><li>Solid understanding of distributed systems and back-end architecture
</li><li>Data Engineering Concepts (IMPORTANT)</li><li>
Strong knowledge of data modelling (OLTP vs OLAP)
</li><li>Strong knowledge of data lake architecture (primary) and data warehousing concepts</li><li>
Strong knowledge of REST APIs and API design (FastAPI / Flask / Django)</li></ul><h4 class="jobSectionHeader"><b>
Core Platforms &amp; Technologies (Required)</b></h4><p><b>
Strong working knowledge of:</b></p><ul><li>
 Relational databases - strong hands-on experience required (e.g. PostgreSQL, MS SQL Server, MySQL)</li><li>
Non-relational / NoSQL databases - working knowledge required (e.g. MongoDB, SQLite, TimescaleDB, Redis)</li><li>
Graph databases - understanding of graph data models (e.g. Apache AGE, Neo4j concepts)</li><li>
Comfortable working across multi-database environments - relational, time-series, graph, and cache layers in a single architecture</li></ul><p>
Good to Have: Hands-on experience with modern data tools such as Kafka, Airbyte, S3/ADLS, Snowflake, or similar platforms</p><h4 class="jobSectionHeader"><b>
Data Ingestion &amp; Processing</b></h4><p><b>
Ability to design and implement:</b></p><ul><li>
Data ingestion pipelines
</li><li>Data transformation layers (ETL/ELT)
</li><li>Unified and scalable data models
</li><li>System Thinking &amp; Architecture</li></ul><p><b>
Ability to:</b></p><ul><li>
Design scalable back-end architectures
</li><li>Define and manage data flow across systems
</li><li>Guide integration strategies between multiple services and platforms</li></ul><p><b>
Good to Have</b></p><ul><li>
Experience with cloud platforms (AWS / GCP / Azure)
</li><li>Exposure to real-time data processing systems
</li><li>Knowledge of ML data pipelines or analytics workflows
</li><li>Experience using Python for data ingestion pipelines, transformation scripts, and automation workflows is an advantage</li></ul><p><b>
What We&rsquo;re Looking For</b></p><ul><li>
Passion for experimenting with new tools, frameworks, and ideas
</li><li>Ability to quickly prototype and iterate on features
</li><li>Interest in blending creativity with engineering
</li><li>Comfort working in less rigid, exploratory development environments
</li><li>Strong curiosity and self-learning mindset</li></ul><p><b>
Bonus Points If You</b></p><ul><li>
Use AI-assisted coding tools (like GitHub Copilot, Cursor, Antigravity, Claude)
</li><li>Build side projects, prototypes, or experimental apps
</li><li>Enjoy rapid MVP development, or creative coding
</li><li>Stay updated with latest trends in AI, and developer tools</li></ul><br>
<p></p>
</div><div><h4 class="jobSectionHeader"><b>Requirements (Qualifications/Experience/Competencies)
</b></h4><p>Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related field. A Master's degree in a relevant field is an advantage<br>
<br>
5 - 7 years of experience</p><p><b>
Ready to Join Us?</b><br>
<br>
If you are passionate about building modern AI-powered products and enjoy working on innovative digital solutions, we would love to hear from you. Immediate joiners are preferred.<br>
<br>
<b>Please note: This position is open to candidates currently residing in the UAE only.</b></p></div></div>

Senior Data & AI Platform Engineer

KAYALI

<div><p><b>Who We Are</b></p><p>
Fueled by passion, KAYALI was founded in 2018 by beauty mogul and fragrance fanatic, Mona Kattan. Translating to &lsquo;my imagination&rsquo; in Arabic, KAYALI provides a modern fragrance experience inspired by Mona&rsquo;s rich Middle Eastern heritage and the art of layering scents to help you create your mood; where sharing scents is a ritual and smelling good is both an act of goodwill and self-love. Mona collaborates with some of the world&rsquo;s most renowned perfumers and sources the finest ingredients to create unique juices that are infinitely memorable, long-lasting, and cruelty-free. Each luxurious fragrance is an ode to true craftsmanship and tells a special story, from the addictive notes to the multi-faceted jeweled bottles.</p><p></p><p><b><br>
Our Mission</b></p><p>
To make everyone feel like the diamond they are! To build a global community of fragrance lovers through the power of scent and by providing them with the most innovative &amp; luxurious fragrances, education and sharing our Middle Eastern fragrance rituals with the world.</p><p></p><p><b><br>
Summary</b></p><p>
Introduction of a new role to provide expertise across data warehousing platforms including Azure Data Bricks and Power BI along with all the necessary integrations. The BI Engineer will be responsible for supporting, implementing and maintaining data warehouse Pipelines and Reports to streamline business process.</p><p></p><p><b><br>
RESPONSIBILITIES:
</b></p><p><b>Data Platform &amp; Architecture</b></p><ul><li>
Manage a scalable modern Data platform leveraging the Azure ecosystem, including Azure Databricks, Azure Synapse Analytics, Azure Data Factory, and Azure Data Lake Storage.
</li><li>Design and implement Lakehouse architecture using Medallion (Bronze, Silver, Gold) patterns with Delta Lake to ensure data reliability, scalability, and performance with knowledge of Delta Lake and ACID-compliant data architectures
</li><li>Apply data modelling techniques (dimensional modelling, star/snowflake schemas) to support both operational and analytical use cases.</li></ul><p></p><p><b>
Data Engineering &amp; Pipelines</b></p><ul><li>
Develop and maintain robust, high-performance data pipelines using cloud-native technologies (Databricks, PySpark, Lakehouse Workflows) to support ingestion, transformation, and curation of large-scale datasets.
</li><li>Knowledge in identifying and resolving complex data discrepancies across multi-stage ETL/ELT pipelines, with a focus on root cause analysis (RCA) of failures, data skew, and late-arriving data etc.
</li><li>Build and optimize end-to-end data integration frameworks, including ingestion from relational databases, flat files, and third-party REST APIs (handling authentication, pagination, and incremental loads).
</li><li>Troubleshoot and resolve complex data pipeline failures, performance bottlenecks, and data inconsistencies, performing root cause analysis in production environments.</li></ul><p></p><p><b>
Business Intelligence &amp; Power BI</b></p><ul><li>
Design, develop, and optimize Power BI datasets, reports, and dashboards, applying best practices in data modelling, DAX, and performance tuning.
</li><li>High-level proficiency in performance tuning and semantic model debugging, specifically using tools like DAX Studio and Tabular Editor to isolate issues within the Filter Context, Row Context, and complex relationship cardinalities.
</li><li>Strategic ability to troubleshoot data issues and investigate Gateway connectivity failures, Direct Query latency issues, and visual-level calculation errors to ensuring report accuracy and faster response times for executive stakeholders.</li></ul><p></p><p><b>
Data Integration &amp; Consumption</b></p><ul><li>
Manage the integration of data platforms with Power BI, ensuring efficient data models and seamless data consumption for reporting and analytics.</li></ul><p></p><p>
<b>Data Governance &amp; Security</b></p><ul><li>
Implement and manage data security frameworks, including role-based access control (RBAC), Row-Level Security, and governance using Unity Catalog or equivalent tools.</li></ul><p></p><p><b>
DevOps, CI/CD &amp; Deployment</b></p><ul><li>
Knowledge in CI/CD pipelines using Azure DevOps or GitHub Actions to enable automated, reliable, and consistent deployment of data solutions.</li></ul><p></p><p><b>
Performance Optimization &amp; Cost Management</b></p><ul><li>
Monitor and optimize data platform performance and cloud resource utilization, identifying cost optimization opportunities and ensuring efficient workload execution.</li></ul><p></p><p><b>
Data Quality &amp; Reliability</b></p><ul><li>
Implement data quality, validation, and monitoring frameworks to ensure accuracy, consistency, and reliability of data assets.</li></ul><p></p><p><b>
Stakeholder Collaboration &amp; Delivery</b></p><ul><li>
Collaborate with cross-functional teams including business stakeholders, analysts, and engineers to translate business requirements into scalable technical solutions.</li></ul><br>
<b>
Requirements</b><br>
<ul><li>
 Minimum 5+ years in both Reporting and Data engineering domain, with at least 3+ years focused on Azure based data platforms mainly Power BI and Azure Databricks.</li><li>
 Comprehensive knowledge of the Azure Data Stack and Strong hands-on experience with Azure Databricks, Azure Data Lake Storage, Synapse, ADF and open-source cloud native tools.</li><li>
 Extensive experience in Power BI, including Dataset design and optimization, Report and dashboard development using calculated measures, DAX etc.</li><li>
Knowledge of performance tuning techniques (Query optimization, Import vs Direct Query, aggregations etc).</li><li>
Proficiency in SQL, PySpark, Python</li><li>
 Solid understanding on Data modelling techniques.</li><li>
 Effective problem-solving skills backed by strong people skills.</li><li>
 Excellent oral and written communication skills.</li></ul><br>
<b>
Benefits</b><br>
<ul><li>
Premium Medical/Dental/Vision coverage</li><li>
Find your Magic Days</li><li>
Volunteer Day</li><li>
Birthday Leave</li><li>
Egg Freezing Benefits</li><li>
Employee discounts on all Kayali products</li><li>
Quarterly product gifting</li></ul><p><i><br>
Kayali is committed to building diverse and inclusive teams and upholding an equal employment workplace that is free from discrimination. We hire stellar individuals regardless of their race, color, ancestry, religion, gender identity, national origin, sexual orientation, age, marital status, medical conditions, disability, or veteran status. If you need reasonable accommodations at any point in the application or interview process, please let us know.</i></p></div>

BI Engineer

Deriv.com

<div><h3 class="jobSectionHeader"><b>Job Information</b></h3>
<ul><li>Job Opening ID ZR_2195_JOB
</li><li>Industry Business Intelligence
</li><li>Date Opened 22/06/2026
</li><li>Job Type Full time
</li><li>City Dubai
</li><li>Province -
</li><li>Country United Arab Emirates
</li><li>Postal Code -</li></ul></div>
<br><div><h3 class="jobSectionHeader"><b>Job Description</b></h3>
<div><div>Data pipelines don&rsquo;t fail in staging. They fail at 3 a.m. on month-end, when compliance needs the numbers and the dashboard is showing yesterday&rsquo;s data. You&rsquo;ll build the pipelines that don&rsquo;t break&mdash;and when they do, you&rsquo;ll know before anyone else does.<br>
<br>
</div><div></div><br>
<div>This isn&rsquo;t a role where you maintain someone else&rsquo;s SQL. You&rsquo;ll build data infrastructure that powers trading decisions, product analytics, compliance, and AI/ML systems across a global fintech. You&rsquo;ll own data accuracy end-to-end, raise the engineering bar for the team, and catch problems before they reach your stakeholders.<br>
<br>
</div><p></p><br>
<br>
<div><b>Why This Matters</b><br>
<br>
</div><div></div><br>
<div>Deriv&rsquo;s mission is Trading for Anyone, Anywhere, Anytime. Millions of traders, around the clock, across regulatory regimes. Every trade generates data. Every data point feeds analytics, compliance checks, fraud detection, and AI systems that serve customers in real time.<br>
<br>
</div><div></div><br>
<div>When a pipeline delivers stale data, a trader sees the wrong price. When a schema drifts undetected, a compliance report goes wrong. When governance is an afterthought, regulators ask questions nobody can answer. Data engineering at Deriv isn&rsquo;t back-office plumbing. It&rsquo;s the infrastructure that the entire business trusts.<br>
<br>
</div><p></p><br>
<br>
<div><b>Why Deriv</b><b><br>
</b><br>
</div><div></div><br>
<div>We&rsquo;re in production, not planning.
<br>
</div><ul><li>Natural language interfaces querying the data warehouse directly&mdash;leaders ask questions in English, answers come back in seconds
<br>
</li><li>Continuous KPI monitoring with anomaly detection surfacing problems before stakeholders notice
<br>
</li><li>Dozens of fraud detection models running against production data, continuously
<br>
</li><li>400+ users on our internal workflow orchestration platform, fed by the data infrastructure you&rsquo;ll help build and scale
<br>
</li></ul><div>We share openly. Deriv ed is where we write about what we&rsquo;re shipping, what breaks, and what we figured out the hard way. You&rsquo;ll join a transformation that&rsquo;s underway, not one waiting for approval.<br>
<br>
</div><p></p><br>
<br>
<div><b>What You&rsquo;ll Do</b><br>
<br>
</div><div></div><br>
<div>Your work cuts across trading and product analytics, compliance and regulatory reporting, AI/ML feature pipelines, and business intelligence. Your placement depends on team needs and your strengths&mdash;your impact won&rsquo;t be limited to one domain.<br>
<br>
</div><div></div><br>
<div><b>Build pipelines that are governed by design</b><b>
</b><br>
</div><ul><li>Design and build ETL/ELT pipelines across batch and real-time workloads using AI-assisted development&mdash;reducing build time without cutting reliability
<br>
</li><li>Bake in observability from the start: freshness checks, completeness monitoring, schema drift detection, lineage tracking, and alerting. Not retrofitted after launch
<br>
</li>Implement automated data quality checks and anomaly detection into every pipeline&mdash;governance built in, not bolted on<br>
<li>
<br>
</li></ul><div></div><br>
<div><b>Own data accuracy before anyone has to ask</b><b>
</b><br>
</div><ul><li>Identify and resolve data issues before they surface to analysts or stakeholders
<br>
</li><li>Define and maintain data contracts: SLAs, SLOs, schema agreements, and producer-consumer alignment
<br>
</li>Handle PII, access control, and auditability correctly in a regulated financial environment<br>
<li>
<br>
</li></ul><div></div><br>
<div><b>Make the platform better than you found it</b><b>
</b><br>
</div><ul><li>Optimise warehouse performance and cost&mdash;query efficiency, partitioning, clustering, orchestration reliability
<br>
</li><li>Build data models designed to scale beyond the immediate use case: dimensional modelling, semantic layers, reusable abstractions
<br>
</li>Spot gaps in the data platform and address them without waiting to be assigned<br>
<li>
<br>
</li></ul><div></div><br>
<div><b>Raise the bar for the team</b><b>
</b><br>
</div><ul><li>Partner with analysts, product, finance, and compliance teams to translate requirements into reliable, governed data products
<br>
</li><li>Peer-review pipeline code and push quality standards higher across the team
<br>
</li>Help onboard new engineers&mdash;share context, catch mistakes early, make others productive faster<br>
<li>
<br>
<br>
</li></ul><p></p><br>
<div><b>Who You Are</b><br>
<br>
</div><div></div><br>
<div><b>You build pipelines, not just queries</b><b>
</b><br>
</div>6+ years in data engineering. You know that writing SQL is the easy part. The hard part is making sure data arrives on time, matches the schema you promised, and doesn&rsquo;t silently break when upstream changes. You think about data quality before someone files a bug.<br>
<ul><li>
<br>
</li></ul><div></div><br>
<div><b>You know the cloud data stack cold</b><b>
</b><br>
</div>GCP, BigQuery, Airflow, Python&mdash;or the equivalent at comparable scale. You&rsquo;ve built and maintained real pipelines across batch and streaming workloads. You&rsquo;ve shipped with dbt or Dataform and understand why transformation layers matter. You use AI coding assistants daily as part of your workflow, not as a novelty.<br>
<ul><li>
<br>
</li></ul><div></div><br>
<div><b>You think in contracts, not assumptions</b><b>
</b><br>
</div>Data modelling techniques like Kimball star schema, Data Vault, or Medallion architecture aren&rsquo;t buzzwords to you&mdash;they&rsquo;re tools you pick based on the problem. You understand that a pipeline without a data contract is a pipeline waiting to fail. Even better if you&rsquo;ve implemented data contract frameworks or schema registries at scale.<br>
<ul><li>
<br>
</li></ul><div></div><br>
<div><b>You fix root causes, not symptoms</b><b>
</b><br>
</div>When data is wrong, you don&rsquo;t patch the dashboard. You trace the problem to its source, fix it systematically, and make sure it doesn&rsquo;t recur. You explain what happened clearly&mdash;to engineers and to non-technical stakeholders who need to trust the numbers again.<br>
<ul><li>
<br>
</li></ul><div></div><br>
<div><b>You make the people around you better</b><b>
</b><br>
</div>You share learnings actively, create reusable resources, and give feedback that helps others ship better code. When a junior engineer is stuck, you unblock them. When a stakeholder&rsquo;s requirements are vague, you ask the right questions and turn ambiguity into a spec.<br>
<ul><li>
<br>
</li></ul><div></div><br>
<div><b>You&rsquo;ve seen what regulated data looks like</b><b>
</b><br>
</div>A fintech, trading, or compliance-heavy background is a strong plus. You know the difference between handling PII in a textbook and handling it when auditors are checking your work. Experience with warehouse cost optimisation and containerised data platforms rounds out the picture.<br>
<ul><li>
<br>
<br>
</li></ul><p></p><br>
<div><b>Tech Stack</b><b>
</b><br>
</div><ul><li>Cloud &amp; Warehouse: GCP, BigQuery
<br>
</li><li>Orchestration: Airflow
<br>
</li><li>Languages: Python, SQL
<br>
</li><li>Transformation: dbt / Dataform
<br>
</li><li>Streaming: Kafka, Pub/Sub
<br>
</li>Practices: CI/CD, version-controlled pipelines, peer review, AI-assisted development<br>
<li>
<br>
</li></ul><p></p><br>
<div><b>The Honest Reality</b><br>
<br>
</div><div></div><br>
<div>This is demanding work. You&rsquo;ll own pipeline reliability in a business where stale data means wrong trading decisions and missed compliance deadlines. You&rsquo;ll debug schema mismatches at month-end when nobody can wait until Monday. You&rsquo;ll push for governance practices when it&rsquo;s easier to just ship the quick fix.<br>
<br>
</div><div></div><br>
<div>But you&rsquo;ll build infrastructure the entire company depends on. You&rsquo;ll see your pipelines powering dashboards, feeding AI models, and passing compliance audits. And you&rsquo;ll work with a team that treats data engineering as a craft, not a cost centre.<br>
<br>
</div><div></div><br>
<div>If you want predictable work and clean datasets handed to you, this isn&rsquo;t it. If you want to build data systems that actually matter, it might be.</div></div></div><p></p>

Senior Data Engineer

Dicetek LLC

<div>
 Role Overview
 <br/>
 <br/>
 We are seeking a highly skilled and motivated Data Engineer with 5&ndash;7 years of experience to design, build, and optimize scalable data pipelines and platforms. The ideal candidate will have strong expertise in SQL, Databricks, Confluent (Kafka), Azure Data Factory (ADF), and Azure Data Lake Storage (ADLS), with a solid understanding of banking domain processes and regulatory considerations. This role involves working closely with cross-functional teams to enable data-driven decision-making across critical banking functions.
 <br/>
 <br/>
 Key Responsibilities
 <br/>
 <br/>
 <ul>
  <li>
   Design, develop, and maintain robust, scalable data pipelines using Azure Data Factory (ADF) and Databricks
  </li>
  <li>
   Implement batch and real-time data ingestion frameworks leveraging Confluent Kafka
  </li>
  <li>
   Build and optimize data models, transformations, and ETL/ELT pipelines using SQL and Spark
  </li>
  <li>
   Develop and manage data storage solutions in Azure Data Lake Storage (ADLS)
  </li>
  <li>
   Ensure high data quality, integrity, and governance standards
  </li>
  <li>
   Support data integration across systems including core banking and CRM
  </li>
  <li>
   Optimize performance of large-scale datasets
  </li>
  <li>
   Collaborate with stakeholders to translate requirements into solutions
  </li>
  <li>
   Ensure compliance with banking regulations and data privacy
  </li>
  <li>
   Troubleshoot production issues
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Skills &amp; Experience
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Strong expertise in SQL (complex queries, tuning, modeling)
  </li>
  <li>
   Hands-on experience with Databricks, Spark, PySpark
  </li>
  <li>
   Experience with Confluent Kafka and event-driven architecture
  </li>
  <li>
   Experience with Azure Data Factory (ADF)
  </li>
  <li>
   Experience with Azure Data Lake Storage (ADLS)
  </li>
  <li>
   Understanding of data warehousing concepts
  </li>
  <li>
   Familiarity with CI/CD and DevOps for data platforms
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Domain Knowledge (Preferred)
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience in banking or financial services
  </li>
  <li>
   Understanding of core banking systems
  </li>
  <li>
   Knowledge of payments, lending, wealth management
  </li>
  <li>
   Awareness of regulatory frameworks (KYC, AML)
   <br/>
   <br/>
  </li>
 </ul>
 Soft Skills
 <br/>
 <br/>
 <ul>
  <li>
   Strong analytical and problem-solving skills
  </li>
  <li>
   Excellent communication and stakeholder engagement
  </li>
  <li>
   Ability to work in fast-paced environments
  </li>
  <li>
   Strong ownership and collaboration mindset
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor&rsquo;s or Master&rsquo;s degree in relevant field
  </li>
  <li>
   Azure/Data engineering certifications preferred
   <br/>
   <br/>
  </li>
 </ul>
 Good to Have
 <br/>
 <br/>
 <ul>
  <li>
   Experience with Python or Scala
  </li>
  <li>
   Exposure to data governance tools
  </li>
  <li>
   Knowledge of APIs and microservices
  </li>
  <li>
   Experience with BI tools like Power BI
  </li>
 </ul>
</div>


Data Engineer

BurjX

<div>
 <p>
  <strong>
   About BurjX
  </strong>
 </p>
 <p>
  BurjX is an ADGM-regulated digital asset brokerage and custodian headquartered in Abu Dhabi. Our mission is to build the most trusted and user-friendly digital asset platform in the region.
 </p>
 <p>
  We are looking for a Senior Full-Stack Engineer to help build and scale our platform across web, mobile, backend services, integrations, and internal operational systems.
 </p>
 <p>
  This is a hands-on role for an experienced engineer who can take ownership of features end-to-end, contribute to technical architecture, and help maintain high engineering standards in a fast-moving, regulated fintech environment.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities
  </strong>
 </p>
 <ul>
  <li>
   Design, build, and maintain product features across frontend, backend, APIs, databases, and third-party integrations.
  </li>
  <li>
   Own features from technical design through deployment and ongoing production support.
  </li>
  <li>
   Develop secure, scalable systems supporting trading, onboarding, KYC/KYB, payments, wallets, custody, and internal operations.
  </li>
  <li>
   Build and maintain integrations with banking partners, payment providers, custody providers, blockchain services, compliance tools, and market data providers.
  </li>
  <li>
   Design and maintain clean APIs, data models, and reusable platform components.
  </li>
  <li>
   Participate in architecture discussions and contribute to technical decision-making.
  </li>
  <li>
   Conduct code reviews and help maintain engineering quality, security, and best practices.
  </li>
  <li>
   Collaborate closely with Product, Compliance, Operations, Customer Support, and other engineering team members.
  </li>
  <li>
   Identify and proactively address technical debt, operational risks, and system bottlenecks.
  </li>
  <li>
   Contribute to release management, testing, documentation, and engineering processes.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Qualifications &amp; Experience
  </strong>
 </p>
 <ul>
  <li>
   5+ years of experience as a Full-Stack Engineer, Software Engineer, or similar role.
  </li>
  <li>
   Strong experience with modern backend technologies such as Node.js and TypeScript.
  </li>
  <li>
   Strong experience with React, Next.js, React Native, or similar frontend frameworks.
  </li>
  <li>
   Experience designing and building REST APIs and secure application architectures.
  </li>
  <li>
   Experience working with relational and/or NoSQL databases such as PostgreSQL, MySQL, MongoDB, or DynamoDB.
  </li>
  <li>
   Strong understanding of authentication, authorization, access control, and application security.
  </li>
  <li>
   Experience working with cloud infrastructure, CI/CD pipelines, containers, and distributed systems.
  </li>
  <li>
   Strong debugging and problem-solving skills across frontend, backend, infrastructure, and integrations.
  </li>
  <li>
   Experience building production systems that manage sensitive customer data or financial transactions.
  </li>
  <li>
   Excellent communication skills and ability to work cross-functionally in a fast-paced environment.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Preferred Experience
  </strong>
 </p>
 <ul>
  <li>
   Fintech, banking, payments, trading, or cryptocurrency experience.
  </li>
  <li>
   Experience working with crypto exchanges, brokerages, custody platforms, or digital asset infrastructure.
  </li>
  <li>
   Familiarity with KYC, AML, sanctions screening, fraud prevention, and compliance workflows.
  </li>
  <li>
   Experience integrating payment rails, banking APIs, or fiat deposit and withdrawal systems.
  </li>
  <li>
   Experience with AWS and cloud-native architectures.
  </li>
  <li>
   Experience with event-driven systems, microservices, queues, and serverless technologies.
  </li>
  <li>
   Experience with monitoring, observability, incident response, and operational tooling.
  </li>
  <li>
   Understanding of blockchain infrastructure, wallets, custody systems, and transaction lifecycles.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   What Success Looks Like
  </strong>
 </p>
 <ul>
  <li>
   Product features are delivered with high quality, reliability, and security.
  </li>
  <li>
   Technical solutions are scalable, maintainable, and aligned with business objectives.
  </li>
  <li>
   Production issues are identified, resolved, and prevented from recurring.
  </li>
  <li>
   Engineering standards and code quality continue to improve.
  </li>
  <li>
   Internal teams can rely on robust tools and systems to operate efficiently.
  </li>
  <li>
   BurjX's platform continues to scale while maintaining a strong user experience and regulatory compliance.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Why Join BurjX
  </strong>
 </p>
 <ul>
  <li>
   Build at one of the region's leading regulated digital asset platforms.
  </li>
  <li>
   Work directly with experienced founders and industry operators.
  </li>
  <li>
   Help shape products used by retail, professional, and institutional clients.
  </li>
  <li>
   Contribute to a fast-growing company operating at the intersection of technology, finance, and digital assets.
  </li>
  <li>
   Competitive compensation and meaningful opportunity for growth.
  </li>
 </ul>
</div>


Full Stack Engineer

Heidelberg Materials Trading

<div>
 <p>
  Heidelberg Materials Trading is the international trading arm of Heidelberg Materials, one of the world&rsquo;s largest building materials companies, handling overseas drybulk product exports and imports across global markets. We are looking for a talented individual to join our IT team in our Dubai Office as:
 </p>
 <p>
 </p>
 <p>
  <strong>
   Data Engineer
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   About the Role:
  </strong>
 </p>
 <p>
  This role sits at the centre of a greenfield transformation initiative, where we are replacing legacy reporting workflows with a modern, Microsoft first cloud data platform. You will work directly with IT and business stakeholders to connect our front office (Salesforce) and back office (JD Edwards ERP) systems into a unified analytics layer powering business critical Power BI dashboards. The role requires a hands on technical developer who builds, and owns data pipelines end-to-end, and can operate pragmatically within a Microsoft Fabric&ndash;oriented environment while proposing justified alternatives when needed.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Key Responsibilities:
  </strong>
 </p>
 <ul>
  <li>
   Lead the migration of reporting from QlikSense to Power BI, ensuring accurate replication of existing logic, KPIs, and reporting workflows.
  </li>
  <li>
   Design, build, and maintain scalable Azure SQL&ndash;based data pipelines to support analytics, reporting, and operational decision making.
  </li>
  <li>
   Build and optimise Power BI data models, reusable datasets, and semantic layers following best practice modelling principles.
  </li>
  <li>
   Integrate data from JD Edwards (ERP), Salesforce (CRM), and other business systems, ensuring accuracy, consistency, and reliability across the reporting lifecycle.
  </li>
  <li>
   Reverse engineer manual Excel-based workflows and translate them into automated SQL or Python based pipelines to improve efficiency and reliability.
  </li>
  <li>
   Implement data validation, quality checks, monitoring, and performance optimisation across the data platform and reporting layers.
  </li>
  <li>
   Deliver Power BI training for business users and create documentation, dashboards, and best practice materials.
  </li>
  <li>
   Work closely with cross-functional teams to understand business requirements and translate them into scalable, maintainable data and reporting solutions.
  </li>
  <li>
   Contribute to data governance, security standards, and consistent documentation practices.
  </li>
  <li>
   Support UAT cycles, phased go lives, and iterative enhancements to reporting processes.
  </li>
 </ul>
 <p>
 </p>
 <p>
  <strong>
   Qualifications:
  </strong>
 </p>
 <ul>
  <li>
   Bachelor&rsquo;s degree in Computer Science, Data Engineering, Information Systems, or a related field.
  </li>
  <li>
   Minimum 3 years of hands‑on experience in data engineering or data platform development. - Minimum 6 years for senior consideration.
  </li>
  <li>
   Strong SQL skills with experience working on relational databases and transformation logic.
  </li>
  <li>
   Hands‑on experience with Power BI (DAX, data modelling, row‑level security, performance optimisation).
  </li>
  <li>
   Experience developing ETL/ELT pipelines using Azure Data Factory, Azure Synapse, or Microsoft Fabric (experience with at least one is required).
  </li>
  <li>
   Experience integrating ERP or CRM systems such as JD Edwards, SAP, or Salesforce, with a strong advantage for candidates familiar with these architectures.
  </li>
  <li>
   Solid understanding of transactional vs analytical modelling and modern data warehousing approaches.
  </li>
  <li>
   Comfortable operating as a hands‑on builder, capable of developing, testing, and shipping data pipelines independently.
  </li>
  <li>
   Strong communication skills with the ability to translate technical detail into clear business language.
  </li>
  <li>
   Able to work within a Microsoft‑first environment while recommending alternative tools when supported by a clear business case.
  </li>
  <li>
   Exposure to Microsoft Power Platform (Power Automate, Power Apps) is an advantage.
  </li>
  <li>
   Experience in trading, logistics, or supply chain environments is beneficial.
  </li>
  <li>
   Familiarity with Azure‑based cloud data architectures is preferred.
  </li>
 </ul>
 <p>
 </p>
 <p>
 </p>
 <p>
  <strong>
   We thank all applicants for their interest, but only shortlisted candidates will be contacted.
  </strong>
 </p>
 <p>
  <strong>
   All applications will be kept strictly confidential.
  </strong>
 </p>
</div>


Datamatics Global Services Ltd

<div><b>Job Description &ndash; Data Quality Engineer (Databricks) &ndash; x 4 Positions</b><br>
<b>Location: Abudhabi - UAE - Onsite</b><br>
<b>Open to Relocate</b><br>
<b>Duration: 6 months ( Extendable to One Year)</b><br>
<b>Experience: 5 to 7 Years</b><br>
<b>Project start date is 1st July - Immediate joiners will be preferred</b><br>
<br>
<b>Role Overview</b><br>
The Data Quality Engineer will be responsible for designing, implementing, and operating ADC's enterprise data quality framework within the Databricks platform. The role will deliver automated profiling, quality rule execution, cleansing, monitoring, remediation support, and quality reporting capabilities across 170 datasets and 1,346 prioritised Critical Data Elements (CDEs).<br>
Working closely with Data Modellers, Data Catalogue Specialists, business data owners, and platform engineers, the Data Quality Engineer will establish scalable and reusable quality controls that improve trust, accuracy, completeness, consistency, timeliness, validity, and uniqueness across ADC's data estate.<br>
<b>Key Responsibilities</b><br>
<b>Databricks Platform Configuration and Administration</b><ul><li>
Configure and manage the Databricks environment supporting enterprise data quality operations.</li><li>
Establish and maintain:<ul><li>
Compute clusters.</li><li>
PySpark notebook frameworks.</li><li>
Delta Lake storage structures.</li><li>
Unity Catalog integration.</li></ul></li><li>
Optimise platform performance for large-scale profiling and rule execution across all in-scope datasets and CDEs.</li><li>
Implement development, testing, and production deployment standards for data quality assets.</li></ul><b>
Data Profiling and Quality Assessment</b><ul><li>
Design and develop AI-assisted profiling notebooks using PySpark.</li><li>
Perform baseline data quality assessments across the six quality dimensions:<ul><li>
Completeness.</li><li>
Accuracy.</li><li>
Consistency.</li><li>
Validity.</li><li>
Timeliness.</li><li>
Uniqueness.</li></ul></li><li>
Capture and analyse:<ul><li>
Null value rates.</li><li>
Duplicate records.</li><li>
Invalid values.</li><li>
Format violations.</li><li>
Outliers.</li><li>
Schema drift.</li></ul></li><li>
Produce quality profiling outputs for all prioritised CDEs and datasets.</li></ul><b>
Data Quality Rule Factory Development</b><ul><li>
Design and implement a reusable Data Quality Rule Factory.</li><li>
Build parameterised PySpark-based rule templates capable of supporting large-scale rule deployment.</li><li>
Enable automated generation and management of approximately 6,730 data quality rules without manual rule-by-rule development.</li><li>
Ensure rules are reusable, configurable, and maintainable across multiple datasets and domains.</li></ul><b>
Data Quality Controls and Pipeline Integration</b><ul><li>
Deploy quality rules as reusable Databricks Jobs integrated into Delta Lake processing pipelines.</li><li>
Embed quality controls within Bronze, Silver, and Gold processing stages.</li><li>
Implement automated quality gates preventing data progression where defined thresholds are not met.</li><li>
Maintain rule traceability and execution history for audit and governance purposes.</li></ul><b>
Data Cleansing and Quality Improvement</b><ul><li>
Develop automated remediation and cleansing pipelines using PySpark.</li><li>
Implement:<ul><li>
Standardisation routines.</li><li>
Data enrichment processes.</li><li>
Deduplication logic.</li><li>
Schema harmonisation controls.</li></ul></li><li>
Deploy machine learning models managed through MLflow for:<ul><li>
Anomaly detection.</li><li>
Exact duplicate detection.</li><li>
Fuzzy matching and duplicate identification.</li></ul></li><li>
Ensure all AI and ML recommendations are explainable, auditable, and routed through human-in-the-loop validation processes where required.</li></ul><b>
Exception Management and Reprocessing</b><ul><li>
Design and manage exception handling processes for failed quality records.</li><li>
Implement quarantine Delta Lake tables serving as the Failed Record Register.</li><li>
Capture and maintain:<ul><li>
Failure reason.</li><li>
Associated CDE.</li><li>
Rule reference.</li><li>
Processing timestamp.</li><li>
Resolution status.</li></ul></li><li>
Develop reprocessing workflows to support correction and controlled re-ingestion of remediated records.</li></ul><b>
Data Quality Metrics and Reporting</b><ul><li>
Develop Delta Lake metric aggregation structures supporting enterprise quality reporting.</li><li>
Calculate and publish:<ul><li>
Data Quality Index (DQI) scores.</li><li>
Dimension-level quality metrics.</li><li>
Rule pass/fail rates.</li><li>
Dataset compliance scores.</li><li>
SLA adherence indicators.</li></ul></li><li>
Provide curated outputs to support Power BI quality dashboards and executive reporting.</li></ul><b>
Monitoring, Alerting and Predictive Quality Management</b><ul><li>
Configure automated quality monitoring and alerting mechanisms.</li><li>
Implement threshold-based notifications using:<ul><li>
Databricks SQL Alerts.</li><li>
Azure Monitor integrations.</li></ul></li><li>
Develop predictive risk scoring models to identify datasets at risk of future quality degradation.</li><li>
Support proactive quality management and operational intervention activities.</li></ul><b>
Root Cause Analysis and Remediation Support</b><ul><li>
Apply Databricks machine learning and pattern analysis techniques to profiling and rule execution outputs.</li><li>
Support AI-assisted root cause analysis across established remediation categories.</li><li>
Identify recurring quality issues, systemic defects, and process breakdowns.</li><li>
Produce prioritised remediation datasets for business and operational stakeholders.</li><li>
Export remediation outputs to Power BI and Excel to support:<ul><li>
Remediation Tiering Matrix.</li><li>
Prioritisation Scoring Models.</li><li>
Governance reporting processes.</li></ul></li></ul><b>
Governance, Compliance and Stakeholder Engagement</b><ul><li>
Collaborate with Data Modellers and Data Catalogue Specialists to ensure quality controls align with authoritative data definitions and metadata standards.</li><li>
Support DDA and DGE governance processes by producing required quality artefacts and evidence.</li><li>
Maintain documentation, version control, and audit trails for all quality assets, rules, models, and processes.</li><li>
Participate in quality reviews, governance forums, and stakeholder workshops.</li></ul><b>
Required Skills and Experience</b><ul><li>
Strong experience designing and implementing enterprise Data Quality frameworks.</li><li>
Advanced Databricks engineering experience.</li><li>
Strong PySpark development skills.</li><li>
Experience with:<ul><li>
Delta Lake.</li><li>
Unity Catalog.</li><li>
Databricks Workflows and Jobs.</li><li>
Databricks SQL.</li></ul></li><li>
Experience building scalable data validation and quality rule frameworks.</li><li>
Knowledge of machine learning techniques for anomaly detection and data quality monitoring.</li><li>
Experience using MLflow for model management and deployment.</li><li>
Strong understanding of data governance, metadata management, and data lifecycle processes.</li><li>
Experience integrating data quality metrics into reporting platforms such as Power BI.</li><li>
Knowledge of cloud-based data engineering and modern lakehouse architectures.</li></ul><b>
Deliverables</b><ul><li>
Configured Databricks Data Quality environment.</li><li>
Enterprise Data Quality Rule Factory.</li><li>
AI-assisted profiling notebooks and baseline assessment outputs.</li><li>
Automated data quality validation and gating processes.</li><li>
Data cleansing and remediation pipelines.</li><li>
Failed Record Register and reprocessing workflows.</li><li>
Data Quality metric aggregation tables.</li><li>
DQI reporting feeds and dashboard datasets.</li><li>
Predictive quality monitoring and alerting solutions.</li><li>
Root Cause Analysis and remediation support datasets.</li><li>
Governance, audit, and compliance artefacts supporting DDA and DGE reviews.</li></ul><p>
</p><p>dmGrkjT88v</p></div>

Data Quality Engineer (Databricks)

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Data Quality Engineer (Databricks)