data scientist

ai engineer

senior engineer

senior project engineer

senior ai engineer

senior data engineer

Parser

<div>
 
 
 Senior Data Engineer
 
 
 
 
 
 
 
 Who is Parser?
 
 
 
 Technology alone does not create impact&mdash;the right teams do. Founded in 2018, Parser is a boutique technology services and consulting firm helping global organizations solve complex business challenges through digital transformation, product development and AI enablement.
 
 
 
 
 
 We are a fast-growing team of 340+ engineers and consultants across Europe (UK, Spain, Portugal), the Americas (US, Argentina, Uruguay, Colombia), and the Middle East. We combine global reach with a mindset focused on agility, senior expertise, and close collaboration.
 
 
 We work as an extension of our clients&rsquo; teams, helping them define the right problems, shape solutions, and deliver technology-driven outcomes that create measurable business value.
 
 
 
 
 
 Our expertise spans software engineering, AI &amp; data, product development, and customer experience, delivered by teams that combine strong technical depth with a consulting mindset.
 
 
 
 
 
 
 Why Join Us?
 
 
 
 If you are looking for a place where you can think beyond execution, take true ownership of outcomes, influence decisions, and continuously learn alongside top-tier specialists in a truly global environment, we&rsquo;d love to meet you.
 
 
 
 
 
 
 How You'll Make an Impact?
 
 
 
 As a Senior Data Engineer you will be participating in the design and build of the next generation of AI-driven data products and intelligence platforms. As a Python-strong, AI-aware Data Engineer, you'll lead the design and development of data ingestion, ETL, and multi-agent analytical systems by creating scalable schemas and transformation layers that power analytics and AI agents across the UAE and beyond.
 
 
 
 
 
 
 Your key responsibilities:
 
 
 <ul>
 <li>
 Build and maintain end-to-end
 
 data ingestion and ETL pipelines
 
 </li>
 <li>
 Ingest structured and unstructured data from APIs, Snowflake, Oracle, documents, and market data providers
 </li>
 <li>
 Develop production-quality Python services and data workflows
 </li>
 <li>
 Orchestrate pipelines using Kubernetes and workflow tools
 </li>
 <li>
 Apply clean architecture and strong engineering design patterns
 </li>
 <li>
 Design schemas and transformation layers for analytics and AI agents
 </li>
 <li>
 Prepare data for
 
 AI and RAG systems
 
 (chunking, embeddings, retrieval pipelines)
 </li>
 </ul>
 
 
 
 
 
 What you&rsquo;ll bring to the role:
 
 
 
 
 
 
 
 Essential Requirements:
 
 
 <ul>
 <li>
 6+ years of experience in Python
 </li>
 <li>
 3+ years of experience with Kubernetes-based workloads
 </li>
 <li>
 ETL/ELT pipeline Design and orchestration (Airflow, ADF, Databricks or similar)
 </li>
 <li>
 Hands on experience with RAG systems, Embeddings and Vector Databases.
 </li>
 <li>
 Experience with Azure cloud
 </li>
 <li>
 Experience with Data Warehouse Architectures, API and Database integrations
 </li>
 </ul>
 
 
 
 
 
 Desirable Requirements:
 
 
 <ul>
 <li>
 Experience with Snowflake and Oracle
 </li>
 <li>
 Kafka/Event hub streaming systems
 </li>
 <li>
 LLM evaluation, prompt engineering
 </li>
 <li>
 Experience building AI or Data Platforms.
 </li>
 </ul>
 
 
 
 
 
 Location:
 
 
 
 Only considering candidates based in
 
 Dubai or Abu Dhabi.
 
 
 
 Hybrid work model
 
 2-3 days
 
 at the office per week.
 
 
 
 
 
 
 You will receive:
 
 
 <ul>
 <li>
 The chance to join an organization with triple-digit growth that is changing the paradigm on how software products are built.
 </li>
 <li>
 The opportunity to be part of an amazing, multicultural community of tech experts.
 </li>
 <li>
 A highly competitive compensation package.
 </li>
 <li>
 A flexible and hybrid working environment.
 </li>
 <li>
 Employment Visa and Medical insurance.
 </li>
 </ul>
 
 
 
 
 Parser is committed to fostering an inclusive workplace and providing equal employment opportunities to all applicants regardless of race, religion, gender, sexual orientation, age, disability, or any other protected characteristic under applicable law.
 
 
 
 
 
 If you require reasonable accommodations during the recruitment process, please let us know and we will work with you to support your participation.
 
 
 
 
 
 By applying to this role, you acknowledge that your personal data will be processed in accordance with Parser&rsquo;s Privacy Notice for recruitment purposes.
 
 
 
 
 
 Parser may use AI-assisted tools during certain stages of the recruitment process to support operational efficiency. Our recruiting teams use AI to streamline note-taking and scheduling. All hiring decisions are made by people, with human review and oversight.
 
 
 
 
 
 Come and join our #ParserCommunity.
 
 
 https://parserdigital.com/
 
 
 Follow us on
 
 Linkedin
 
 
 
 
 
</div>

Senior Data Engineer

Lunate

<div>
 
 
 Firm Overview
 
 
 
 
 
 
 Lunate is a new Abu Dhabi-based, Partner-led, independent global alternative investment manager with more than 250 employees and $115 billion of assets under management. Lunate invests across the entire private markets spectrum including buyouts, growth equity, early and late-stage venture capital, private credit, real assets, and public equities and public credit. Lunate aims to be one of the world&rsquo;s leading private markets solutions providers through SMAs and multi-asset class funds, seeking to generate best-in-class risk-adjusted returns for its clients.
 
 
 
 
 
 
 Job Purpose
 
 
 
 
 
 
 The Senior Data Engineer will be the first dedicated internal data engineering hire in Lunate&rsquo;s Data &amp; AI function. This role is responsible for building dependable ingestion and transformation pipelines into Snowflake, shaping high-quality, well-modelled datasets that power reporting, operations, and AI-enabled products. You will work closely with the Head of Data &amp; AI and partner with Infrastructure (who own Snowflake account and platform setup) to deliver a modern, data-as-code engineering practice with strong controls, testing, and repeatability, supporting both BI and AI/Agentic workloads.
 
 
 
 
 
 
 Key Duties and Responsibilities
 
 
 
 
 
 <ul>
 <li>
 Build and own ingestion pipelines into Snowflake using modern ELT patterns (Fivetran or equivalent, plus custom ingestion where needed), handling schema drift and source changes safely.
 </li>
 <li>
 Leverage AI Tools to design and automate our pipelines.
 </li>
 <li>
 Implement a layered data architecture (raw, standardised, business) that preserves immutability, auditability, and supports reprocessing and backfills.
 </li>
 <li>
 Develop transformations as version-controlled code (SQL and Python) using dbt or equivalent tooling.
 </li>
 <li>
 Apply pragmatic data modelling practices, using dimensional (Kimball) or relational approaches where appropriate.
 </li>
 <li>
 Build data quality and reconciliation into pipelines, with automated checks and visible quality signals.
 </li>
 <li>
 Operate pipelines like software: CI/CD, automated testing, peer review, and controlled promotion across environments.
 </li>
 <li>
 Optimise Snowflake usage for performance and cost, working with Infrastructure on governance controls.
 </li>
 <li>
 Partner with BI practitioners and AI engineers to deliver AI-ready datasets with consistent semantics and clear lineage.
 </li>
 <li>
 Collaborate with investment, operations, and finance teams to translate requirements into robust data products.
 </li>
 <li>
 Contribute to engineering standards, patterns, and documentation as the data function scales.
 </li>
 </ul>
 
 
 
 
 
 Qualifications and Experience
 
 
 
 
 
 <ul>
 <li>
 6+ years of professional data engineering experience delivering production pipelines.
 </li>
 <li>
 Strong experience with Snowflake&rsquo;s full suite of products &ndash; especially core functionality.
 </li>
 <li>
 Hands-on experience with dbt, including testing and environment management.
 </li>
 <li>
 Familiarity with modern ingestion tools such as Fivetran.
 </li>
 <li>
 Strong grounding in data modelling fundamentals (Kimball and relational).
 </li>
 <li>
 Python-native, comfortable writing production-quality pipeline and orchestration code.
 </li>
 <li>
 Strong software engineering discipline applied to data (Git, CI/CD, automated testing).
 </li>
 <li>
 Experience building replayable, idempotent pipelines with backfill support.
 </li>
 <li>
 Financial services experience preferred, with understanding of audit, restatement, and as-of data concepts.
 </li>
 </ul>
</div>

TALENTMATE

<div>
 
 Job Description
 
 
 
 
 Job Description
 
 
 
 
 Overview
 
 
 
 
 About Presight
 
 
 
 Presight is an ADX-listed public company with Abu Dhabi based G42 as its majority shareholder and is the region&rsquo;s leading big data analytics company powered by GenAI. It combines big data, analytics, and AI expertise to serve every sector, of every scale, to create business and positive societal impact. Presight excels at all-source data interpretation to support insight-driven decision-making that shapes policy and creates safer, healthier, happier, and more sustainable societies. Today, through its range of GenAI-driven products and solutions, Presight is bringing Applied AI to the private and public sector, enabling them to realize their AI strategy and ambitions faster.
 
 
 
 The Opportunity
 
 
 
 Presight is looking for an astute, proficient and qualified Senior Data Engineer to assess, analyze and work with data concepts, use-cases &amp; complex new data sources to provide business insights to customers and support the implementation &amp; integration of the data sources into the Presight platform.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Solve challenging problems, using python coding skills.
 </li>
 <li>
 Design, build and launch new data extraction, transformation &amp; loading processes in production.
 </li>
 <li>
 Web crawling, data cleaning, data annotation, data ingestion and data processing.
 </li>
 <li>
 Reading and collating complex data sets.
 </li>
 <li>
 Creating and maintaining data pipelines.
 </li>
 <li>
 Continual focus on process improvement to drive efficiency and productivity within the team.
 </li>
 <li>
 Use of Python, SQL, ES, Shell etc. to build the infrastructure required for optimal extraction, transformation, and loading of data.
 </li>
 <li>
 Provide insights into key business performance metrics by building analytical tools that utilize the data pipeline.
 </li>
 <li>
 Support the wider business with their data needs on an ad hoc basis.
 </li>
 <li>
 Comply with QHSE (Quality Health Safety and Environment), Business Continuity, Information Security, Privacy, Risk, Compliance Management and Governance of Organizations policies, procedures, plans and related risk assessments.
 
 
 </li>
 </ul>
 
 Qualifications:
 
 
 
 <ul>
 <li>
 Bachelor's degree in computer engineering, Computer Science, or Electrical Engineering and Computer Sciences.
 </li>
 <li>
 6+ years of programming experience, solid coding skills in Python, Shell, and Java
 </li>
 <li>
 Good corporate capacity, good communication skills.
 </li>
 <li>
 Experience with Web crawling, cleaning.
 </li>
 <li>
 Experience with solution architecture, data ingestion, query optimization, data segregation, ETL, ELT, AWS, EC2, S3, SQS, lambda, Elastic Search, Redshift, CI/CD frameworks and workflows.
 </li>
 <li>
 Working knowledge of data platform concepts - data lake, data warehouse, ETL, big data processing (designing and supporting variety/velocity/volume), real time processing architecture for data platforms, scheduling and monitoring of ETL/ELT jobs
 </li>
 <li>
 PostgreSQL and programming (preferably Java, Python), proficiency in understanding data, entity relationships, structured &amp; unstructured data, SQL and NoSQL databases
 </li>
 <li>
 Knowledge of best practice in optimizing columnar and distributed data processing system and infrastructure
 </li>
 <li>
 Experienced in designing and implementing dimensional modelling
 </li>
 <li>
 Knowledge of machine learning and data mining techniques in one or more areas of statistical modelling, text mining and information retrieval.
 
 
 </li>
 </ul>
 Job Details
 
 
 Role Level: Not Applicable Work Type: Full-Time Country: United Arab Emirates City: Dubai Company Website: https://www.presight.ai Job Function: Data Science &amp; AI Company Industry/
 
 
 Sector: Other
 
 
 What We Offer
 
 
 
 About The Company
 
 
 
 Searching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand.
 
 
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 Disclaimer: talentmate.com is only a platform to bring jobseekers &amp; employers together. Applicants are advised to research the bonafides of the prospective employer independently. We do NOT endorse any requests for money payments and strictly advice against sharing personal or bank related information. We also recommend you visit Security Advice for more information. If you suspect any fraud or malpractice, email us at abuse@talentmate.com.
</div>

Deriv

<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.
 
 
 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.
 
 
 
 Why This Matters
 
 
 
 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.
 
 
 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.
 
 
 
 Why Deriv
 
 
 
 We&rsquo;re in production, not planning.
 
 
 <ul>
 <li>
 Natural language interfaces querying the data warehouse directly&mdash;leaders ask questions in English, answers come back in seconds
 </li>
 <li>
 Continuous KPI monitoring with anomaly detection surfacing problems before stakeholders notice
 </li>
 <li>
 Dozens of fraud detection models running against production data, continuously
 </li>
 <li>
 400+ users on our internal workflow orchestration platform, fed by the data infrastructure you&rsquo;ll help build and scale
 
 
 
 </li>
 </ul>
 We share openly. Deriv&acirc;&uml;ed&acirc;&copy; 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.
 
 
 
 What You&rsquo;ll Do
 
 
 
 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.
 
 
 
 Build pipelines that are governed by design
 
 
 
 <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
 </li>
 <li>
 Bake in observability from the start: freshness checks, completeness monitoring, schema drift detection, lineage tracking, and alerting. Not retrofitted after launch
 </li>
 <li>
 Implement automated data quality checks and anomaly detection into every pipeline&mdash;governance built in, not bolted on
 
 
 
 </li>
 </ul>
 
 Own data accuracy before anyone has to ask
 
 
 
 <ul>
 <li>
 Identify and resolve data issues before they surface to analysts or stakeholders
 </li>
 <li>
 Define and maintain data contracts: SLAs, SLOs, schema agreements, and producer-consumer alignment
 </li>
 <li>
 Handle PII, access control, and auditability correctly in a regulated financial environment
 
 
 
 </li>
 </ul>
 
 Make the platform better than you found it
 
 
 
 <ul>
 <li>
 Optimise warehouse performance and cost&mdash;query efficiency, partitioning, clustering, orchestration reliability
 </li>
 <li>
 Build data models designed to scale beyond the immediate use case: dimensional modelling, semantic layers, reusable abstractions
 </li>
 <li>
 Spot gaps in the data platform and address them without waiting to be assigned
 
 
 
 </li>
 </ul>
 
 Raise the bar for the team
 
 
 
 <ul>
 <li>
 Partner with analysts, product, finance, and compliance teams to translate requirements into reliable, governed data products
 </li>
 <li>
 Peer-review pipeline code and push quality standards higher across the team
 </li>
 <li>
 Help onboard new engineers&mdash;share context, catch mistakes early, make others productive faster
 
 
 
 </li>
 </ul>
 
 Who You Are
 
 
 
 
 You build pipelines, not just queries
 
 
 
 <ul>
 <li>
 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.
 
 
 
 </li>
 </ul>
 
 You know the cloud data stack cold
 
 
 
 <ul>
 <li>
 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.
 
 
 
 </li>
 </ul>
 
 You think in contracts, not assumptions
 
 
 
 <ul>
 <li>
 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.
 
 
 
 </li>
 </ul>
 
 You fix root causes, not symptoms
 
 
 
 <ul>
 <li>
 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.
 
 
 
 </li>
 </ul>
 
 You make the people around you better
 
 
 
 <ul>
 <li>
 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.
 
 
 
 </li>
 </ul>
 
 You&rsquo;ve Seen What Regulated Data Looks Like
 
 
 
 <ul>
 <li>
 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.
 
 
 
 </li>
 </ul>
 
 Tech Stack
 
 
 
 <ul>
 <li>
 Cloud &amp; Warehouse: GCP, BigQuery
 </li>
 <li>
 Orchestration: Airflow
 </li>
 <li>
 Languages: Python, SQL
 </li>
 <li>
 Transformation: dbt / Dataform
 </li>
 <li>
 Streaming: Kafka, Pub/Sub
 </li>
 <li>
 Practices: CI/CD, version-controlled pipelines, peer review, AI-assisted development
 
 
 
 </li>
 </ul>
 
 The Honest Reality
 
 
 
 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.
 
 
 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.
 
 
 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>

Deriv.com

<div><h3 class="jobSectionHeader">Job Information</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>
 <div><h3 class="jobSectionHeader">Job Description</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. 
 
</div><div></div> 
<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. 
 
</div> 
 
<div>Why This Matters 
 
</div><div></div> 
<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. 
 
</div><div></div> 
<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. 
 
</div> 
 
<div>Why Deriv 
 
</div><div></div> 
<div>We&rsquo;re in production, not planning.
 
</div><ul><li>Natural language interfaces querying the data warehouse directly&mdash;leaders ask questions in English, answers come back in seconds
 
</li><li>Continuous KPI monitoring with anomaly detection surfacing problems before stakeholders notice
 
</li><li>Dozens of fraud detection models running against production data, continuously
 
</li><li>400+ users on our internal workflow orchestration platform, fed by the data infrastructure you&rsquo;ll help build and scale
 
</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. 
 
</div> 
 
<div>What You&rsquo;ll Do 
 
</div><div></div> 
<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. 
 
</div><div></div> 
<div>Build pipelines that are governed by design
 
</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
 
</li><li>Bake in observability from the start: freshness checks, completeness monitoring, schema drift detection, lineage tracking, and alerting. Not retrofitted after launch
 
</li>Implement automated data quality checks and anomaly detection into every pipeline&mdash;governance built in, not bolted on 
<li>
 
</li></ul><div></div> 
<div>Own data accuracy before anyone has to ask
 
</div><ul><li>Identify and resolve data issues before they surface to analysts or stakeholders
 
</li><li>Define and maintain data contracts: SLAs, SLOs, schema agreements, and producer-consumer alignment
 
</li>Handle PII, access control, and auditability correctly in a regulated financial environment 
<li>
 
</li></ul><div></div> 
<div>Make the platform better than you found it
 
</div><ul><li>Optimise warehouse performance and cost&mdash;query efficiency, partitioning, clustering, orchestration reliability
 
</li><li>Build data models designed to scale beyond the immediate use case: dimensional modelling, semantic layers, reusable abstractions
 
</li>Spot gaps in the data platform and address them without waiting to be assigned 
<li>
 
</li></ul><div></div> 
<div>Raise the bar for the team
 
</div><ul><li>Partner with analysts, product, finance, and compliance teams to translate requirements into reliable, governed data products
 
</li><li>Peer-review pipeline code and push quality standards higher across the team
 
</li>Help onboard new engineers&mdash;share context, catch mistakes early, make others productive faster 
<li>
 
 
</li></ul> 
<div>Who You Are 
 
</div><div></div> 
<div>You build pipelines, not just queries
 
</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. 
<ul><li>
 
</li></ul><div></div> 
<div>You know the cloud data stack cold
 
</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. 
<ul><li>
 
</li></ul><div></div> 
<div>You think in contracts, not assumptions
 
</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. 
<ul><li>
 
</li></ul><div></div> 
<div>You fix root causes, not symptoms
 
</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. 
<ul><li>
 
</li></ul><div></div> 
<div>You make the people around you better
 
</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. 
<ul><li>
 
</li></ul><div></div> 
<div>You&rsquo;ve seen what regulated data looks like
 
</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. 
<ul><li>
 
 
</li></ul> 
<div>Tech Stack
 
</div><ul><li>Cloud &amp; Warehouse: GCP, BigQuery
 
</li><li>Orchestration: Airflow
 
</li><li>Languages: Python, SQL
 
</li><li>Transformation: dbt / Dataform
 
</li><li>Streaming: Kafka, Pub/Sub
 
</li>Practices: CI/CD, version-controlled pipelines, peer review, AI-assisted development 
<li>
 
</li></ul> 
<div>The Honest Reality 
 
</div><div></div> 
<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. 
 
</div><div></div> 
<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. 
 
</div><div></div> 
<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>

amana

<div>
 
 
 Role Summary:
 
 
 
 
 
 
 We are seeking a highly skilled Senior Python Data Engineer to join our Data &amp; Analytics team. This role is focused on designing, building, and maintaining enterprise-grade data pipelines, integrations, data warehouse solutions, and reporting datasets that support operational reporting, business intelligence, analytics, and decision-making across the organization. The ideal candidate combines strong software engineering practices with deep data engineering expertise and is comfortable working across the full data lifecycle, from source system integration and ETL development to data warehousing and reporting enablement
 
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 
 
 
 Data Engineering &amp; Integration:
 
 
 <ul>
 <li>
 Design, develop, and maintain scalable ETL/ELT pipelines using Python.
 </li>
 <li>
 Build integrations with a variety of data sources including APIs, relational databases, flat files, and third-party systems.
 </li>
 <li>
 Implement data extraction, transformation, validation, and loading processes. Develop incremental loading and change-tracking mechanisms.
 </li>
 <li>
 Troubleshoot, optimize, and enhance existing data pipelines.
 </li>
 </ul>
 
 
 
 
 
 Data Platform Development
 
 :
 
 <ul>
 <li>
 Develop reusable and maintainable data processing components.
 </li>
 <li>
 Build and enhance internal data integration and orchestration capabilities.
 </li>
 <li>
 Participate in solution design, architecture reviews, and technical discussions.
 </li>
 <li>
 Produce well-structured, testable, and documented code. Contribute to engineering standards and best practices.
 </li>
 </ul>
 
 
 
 
 
 Workflow Orchestration
 
 
 <ul>
 <li>
 Design and implement Apache Airflow DAGs.
 </li>
 <li>
 Build reliable workflow orchestration and scheduling processes.
 </li>
 <li>
 Monitor pipeline execution and resolve operational issues.
 </li>
 <li>
 Improve observability, reliability, and performance of data workflows.
 </li>
 </ul>
 
 
 
 
 
 Data Warehousing &amp; Reporting
 
 
 <ul>
 <li>
 Design, build, and maintain data warehouse structures that support reporting and analytics requirements.
 </li>
 <li>
 Develop reporting-ready datasets and data models.
 </li>
 <li>
 Optimize SQL queries and warehouse performance.
 </li>
 <li>
 Support the delivery of operational, management, and analytical reporting requirements.
 </li>
 <li>
 Collaborate with business stakeholders, analysts, and reporting teams to ensure data accuracy and usability.
 </li>
 <li>
 Assist in troubleshooting reporting and data quality issues when required
 </li>
 </ul>
 
 
 
 
 
 Engineering Excellence
 
 
 <ul>
 <li>
 Build and maintain Docker images and containerized applications.
 </li>
 <li>
 Participate in code reviews and technical knowledge sharing.
 </li>
 <li>
 Develop unit tests and integration tests.
 </li>
 <li>
 Utilize Git-based development workflows and version control best practices.
 </li>
 <li>
 Support CI/CD and deployment processes where required
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 
 - Has 5+ years of experience, good understanding of Python, Apache Airflow &ndash; Power BI &ndash; APIs (Rest API) and database
 
 
 - Candidates should already be based in the UAE
 
 
 - Strong influencing, leadership, and communication skills with the ability to operate effectively across both technical and non-technical audiences
 
 
 - Excellent attention to detail, problem-solving, and analytical skills.
 
 
 - Strong communication skills and ability to work effectively in a remote environment.
 
 
 
 
 
 
 Preferred Qualifications:
 
 
 
 Good understanding of the stock market, database systems with an understanding of trading systems, MT Platforms (MT4, MT5) and accounts.
 
</div>

Senior Python Data Engineer

Robusta

<div>About the Role
We are seeking an experienced Senior Databricks Data Quality Engineer to lead the design, implementation, and automation of enterprise-scale data quality frameworks within a Databricks environment. The successful candidate will play a key role in establishing data quality controls, profiling frameworks, remediation processes, and AI-assisted quality monitoring across a large-scale data platform consisting of 170+ datasets and over 1,300 Critical Data Elements (CDEs).
This role requires strong expertise in Databricks, PySpark, Delta Lake, MLflow, and modern data quality management practices.
Key ResponsibilitiesData Platform &amp; Databricks Configuration<ul><li>
Configure and manage Databricks workspaces, compute clusters, PySpark notebooks, Delta Lake architecture, and Unity Catalog integrations.
</li><li>Design scalable data quality processing frameworks across 170+ datasets and 1,346 prioritized Critical Data Elements (CDEs).
</li></ul>Data Profiling &amp; Quality Assessment<ul><li>
Develop AI-assisted profiling notebooks using PySpark to establish baseline data quality scores.
</li><li>Assess data quality across six key dimensions including:
</li><li><ul><li>Completeness
</li><li>Uniqueness
</li><li>Validity
</li><li>Consistency
</li><li>Accuracy
</li><li>Timeliness
</li></ul></li><li>Analyze null rates, duplicate records, invalid values, format violations, outliers, and schema drift.
</li></ul>Data Quality Rule Framework<ul><li>
Design and build a scalable Data Quality Rule Factory using parameterized PySpark functions.
</li><li>Enable automated deployment of over 6,700 data quality rules without manual rule-by-rule development.
</li><li>Create reusable rule templates across datasets and data quality dimensions.
</li></ul>Pipeline Quality Enforcement<ul><li>
Integrate data quality controls within Bronze, Silver, and Gold Delta Lake layers.
</li><li>Implement quality gates that prevent data progression unless predefined thresholds are met.
</li><li>Develop reusable Databricks Jobs for automated validation and monitoring.
</li></ul>Data Cleansing &amp; AI-Driven Remediation<ul><li>
Build automated data cleansing pipelines for:
</li><li><ul><li>Standardization
</li><li>Deduplication
</li><li>Schema harmonization
</li></ul></li><li>Deploy MLflow-managed machine learning models for:
</li><li><ul><li>Anomaly detection
</li><li>Fuzzy duplicate detection
</li><li>Exact duplicate identification
</li></ul></li><li>Ensure explainability of model outputs and support human-in-the-loop validation processes.
</li></ul>Exception Management<ul><li>
Design failed-record handling frameworks and quarantine Delta tables.
</li><li>Capture failure reasons, affected CDEs, rule references, and timestamps.
</li><li>Develop automated reprocessing mechanisms for corrected records.
</li></ul>Data Quality Monitoring &amp; Reporting<ul><li>
Build Delta Lake aggregation tables for data quality metrics.
</li><li>Deliver data quality KPIs to Power BI dashboards including:
</li><li><ul><li>Dimension-level scores
</li><li>Rule pass/fail rates
</li><li>SLA adherence metrics
</li></ul></li><li>Configure automated alerting using Databricks SQL Alerts and Azure Monitor.
</li></ul>Predictive Data Quality Analytics<ul><li>
Develop predictive models to identify datasets at risk of quality degradation.
</li><li>Support AI-assisted Root Cause Analysis (RCA) using profiling outputs and machine learning techniques.
</li>Export and prepare remediation datasets for prioritization and governance reporting. 
<li>
</li></ul> 
 
Requirements 
<ul><li>
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
</li><li>5+ years of experience in Data Engineering or Data Quality Engineering.
</li><li>3+ years of hands-on experience with Databricks and PySpark.
</li><li>Strong expertise in Delta Lake architecture and data pipeline development.
</li><li>Experience with Unity Catalog implementation and governance.
</li><li>Hands-on experience with MLflow and machine learning deployment.
</li><li>Strong SQL skills and data modeling expertise.
</li><li>Experience building enterprise-scale data quality frameworks.
</li><li>Experience integrating Databricks with Power BI and Azure services.
</li><li>Strong understanding of data governance, metadata management, and data quality dimensions.
</li></ul>Preferred Qualifications<ul><li>
Microsoft Azure certifications.
</li><li>Databricks Certified Data Engineer Associate or Professional.
</li><li>Experience with enterprise data governance programs.
</li><li>Experience implementing AI-assisted data quality and remediation solutions.
</li><li>Knowledge of Master Data Management (MDM) principles.</li></ul></div>

Senior Data Quality Engineer (4 Months Contract ) Onsite in UAE - Octopus by RTG

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Senior Data Quality Engineer (4 Months Contract ) Onsite in UAE - Octopus by RTG