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data quality engineer databricks

Datamatics Global Services Ltd

<div>Job Description &ndash; Data Quality Engineer (Databricks) &ndash; x 4 Positions 
Location: Abudhabi - UAE - Onsite 
Open to Relocate 
Duration: 6 months ( Extendable to One Year) 
Experience: 5 to 7 Years 
Project start date is 1st July - Immediate joiners will be preferred 
 
Role Overview 
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). 
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. 
Key Responsibilities 
Databricks Platform Configuration and Administration<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>
Data Profiling and Quality Assessment<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>
Data Quality Rule Factory Development<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>
Data Quality Controls and Pipeline Integration<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>
Data Cleansing and Quality Improvement<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>
Exception Management and Reprocessing<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>
Data Quality Metrics and Reporting<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>
Monitoring, Alerting and Predictive Quality Management<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>
Root Cause Analysis and Remediation Support<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>
Governance, Compliance and Stakeholder Engagement<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>
Required Skills and Experience<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>
Deliverables<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>
dmGrkjT88v</div>

Data Quality Engineer (Databricks)

Dicetek LLC

<div>
 Role Overview
 
 
 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.
 
 
 Key Responsibilities
 
 
 <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
 
 
 </li>
 </ul>
 
 Required Skills &amp; Experience
 
 
 
 <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
 
 
 </li>
 </ul>
 
 Domain Knowledge (Preferred)
 
 
 
 <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)
 
 
 </li>
 </ul>
 Soft Skills
 
 
 <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
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in relevant field
 </li>
 <li>
 Azure/Data engineering certifications preferred
 
 
 </li>
 </ul>
 Good to Have
 
 
 <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

KAYALI

<div>Who We Are
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. 
Our Mission
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. 
Summary
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. 
RESPONSIBILITIES:
Data Platform &amp; Architecture<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>
Data Engineering &amp; Pipelines<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>
Business Intelligence &amp; Power BI<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>
Data Integration &amp; Consumption<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>
Data Governance &amp; Security<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>
DevOps, CI/CD &amp; Deployment<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>
Performance Optimization &amp; Cost Management<ul><li>
Monitor and optimize data platform performance and cloud resource utilization, identifying cost optimization opportunities and ensuring efficient workload execution.</li></ul>
Data Quality &amp; Reliability<ul><li>
Implement data quality, validation, and monitoring frameworks to ensure accuracy, consistency, and reliability of data assets.</li></ul>
Stakeholder Collaboration &amp; Delivery<ul><li>
Collaborate with cross-functional teams including business stakeholders, analysts, and engineers to translate business requirements into scalable technical solutions.</li></ul> 

Requirements 
<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> 

Benefits 
<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> 
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.</div>

BI Engineer

<div>Job Description &ndash; 1 x Data Modeller (Erwin Data Modeler) 
Location: Abudhabi - UAE - Onsite 
Open to Relocate 
Duration: 6 months ( Extendable to One Year) 
Experience: 5 to 7 Years 
Project start date is 1st July - Immediate joiners will be preferred 
 
Role Overview 
The Data Modeller will be responsible for establishing and managing the enterprise data modelling capability for ADC using Erwin Data Modeler. The role will lead the design and delivery of authoritative data register models across conceptual, logical, and physical layers while ensuring alignment with government data standards, enterprise architecture requirements, and target platform implementations. 
 
Key Responsibilities 
Erwin Environment Setup and Governance<ul><li>
Configure and administer the Erwin Data Modeler environment for ADC.</li><li>
Establish and maintain the Erwin Mart repository for version-controlled storage of all Data Domain Model (DDM) artefacts.</li><li>
Implement DDA-approved modelling standards, naming conventions, and notation frameworks.</li><li>
Validate accessibility and operational readiness of the modelling environment for both onshore and offshore delivery teams.</li></ul>
Authoritative Data Register Definition<ul><li>
Facilitate stakeholder workshops to identify, scope, and define authoritative data registers.</li><li>
Document domain boundaries, business definitions, ownership, stewardship responsibilities, and relationships with upstream and downstream systems.</li><li>
Produce Authoritative Data Register Definition Packs for each register.</li></ul>
Conceptual, Logical and Physical Data Modelling<ul><li>
Develop conceptual data models representing high-level entities, relationships, and business rules.</li><li>
Produce supporting Data Requirements Specifications for each conceptual model.</li><li>
Develop logical data models defining entities, attributes, relationships, cardinality, and data types.</li><li>
Ensure traceability between conceptual and logical models using Erwin traceability features.</li><li>
Develop physical data models aligned to ADC&rsquo;s target architecture, including:<ul><li>
IBM DB2 Warehouse schemas.</li><li>
Databricks Delta Lake structures supporting Bronze, Silver, and Gold data pipelines.</li></ul></li><li>
Reverse engineer existing DB2 database structures where required to support reconciliation between current and target-state designs.</li></ul>
Data Traceability and Standards Alignment<ul><li>
Maintain complete traceability between approved register definitions and conceptual, logical, and physical models.</li><li>
Manage DDM ID Mapping Tables and Erwin Mart version logs.</li><li>
Align data models with the Unified Data Model (UDM) and Government Information Model (GIM).</li><li>
Maintain the GIM/UDM Alignment Register and identify gaps, conflicts, or enhancement opportunities.</li></ul>
Integration with Enterprise Data Governance<ul><li>
Collaborate with Data Catalogue Specialists to ensure data structures are accurately represented in Microsoft Purview.</li><li>
Support glossary alignment, schema consistency, metadata accuracy, and lineage requirements.</li><li>
Ensure authoritative register entities and attributes are consistently represented across modelling and catalogue platforms.</li></ul>
Review, Approval and Compliance<ul><li>
Prepare modelling artefacts for formal DDA review and approval.</li><li>
Produce Modelling Approval Packages including:<ul><li>
Erwin exports.</li><li>
DDM ID Mapping Tables.</li><li>
Data Requirements Specifications.</li><li>
GIM/UDM Alignment Register.</li></ul></li><li>
Manage review cycles, incorporate feedback, and maintain version control until formal sign-off is achieved.</li></ul>
Physical Model Controls<ul><li>
Ensure all physical models include audit columns, logging requirements, and error-handling specifications.</li><li>
Validate physical model structures against Databricks pipeline implementation requirements and enterprise data governance standards.</li></ul>
Required Skills and Experience<ul><li>
Extensive experience with Erwin Data Modeler and Erwin Mart.</li><li>
Strong background in conceptual, logical, and physical data modelling.</li><li>
Experience with enterprise data governance and metadata management frameworks.</li><li>
Knowledge of IBM DB2 Warehouse and Databricks Delta Lake architectures.</li><li>
Experience implementing data modelling standards and traceability controls.</li><li>
Strong stakeholder engagement and workshop facilitation skills.</li><li>
Understanding of government data standards, information models, and enterprise architecture practices.</li></ul>
Deliverables<ul><li>
Erwin Mart repository and modelling standards configuration.</li><li>
Authoritative Data Register Definition Packs.</li><li>
Conceptual, logical, and physical data models.</li><li>
Data Requirements Specifications.</li><li>
DDM ID Mapping Tables.</li><li>
GIM/UDM Alignment Register.</li><li>
Modelling Approval Packages and DDA sign-off artefacts.</li></ul>
0sESuOhEZS</div>

Data Modeller (Erwin Data Modeler)

Brain Co.

<div>
 
 About Brain Co.
 
 
 
 Brain Co. is an Applied AI startup founded by Elad Gil and Jared Kushner, and backed by many of Silicon Valley&rsquo;s leading builders &mdash; including Patrick Collison (CEO of Stripe), Andrej Karpathy (Cofounder of OpenAI), Mike Krieger (CPO of Anthropic), Kevin Weil (CPO of OpenAI), and Aravind Srinivas (CEO of Perplexity).We are building an AI platform and applications for the world&rsquo;s most important institutions - delivering impact on real-world problems.Our progress so far
 
 
 <ul>
 <li>
 Automated construction permitting for a sovereign government &rarr; 80% faster, unlocking $375M+ in value
 </li>
 <li>
 Optimized supply chains for a leading global energy company &rarr; 30% lower cost, 99% reliability, preventing $100M+ in losses
 </li>
 <li>
 Streamlined hospital patient care across national health systems &rarr; 40% better outcomes, 80% less admin work
 </li>
 <li>
 Raised a $30M Series A from top investors
 </li>
 <li>
 Built a team of 40+ AI experts from Tesla, Google DeepMind, NVIDIA, and Databricks
 
 
 </li>
 </ul>
 At Brain Co., your work will be deployed in the real world, not stuck in research. We move fast, with more demand than we can serve, and are looking for exceptional people to take ownership from day one.
 
 
 
 About The Role
 
 
 
 As an AI/ML Engineer at Brain Co., you will play a crucial role in deploying state-of-the-art models to automate various real world problems in sectors such as healthcare, government and energy. Part of the role will involve turning research breakthroughs into practical solutions for various nation states. This role is your opportunity to make a significant impact by making AI technology both accessible and influential.
 
 
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Innovate and Deploy: Design and deploy advanced LLM models to tackle real-world problems, particularly in automating complex, manual processes in a range of real-world verticals.
 </li>
 <li>
 Optimize and Scale: Build scalable data pipelines, optimize models for performance and accuracy, and prepare them for production. Monitor and maintain deployed models to ensure they continue delivering value across various governments worldwide.
 </li>
 <li>
 Make a Difference: Engage in projects including but not limited to optimizing the world's most advanced energy production systems, modernizing core government workflows, or improving patient outcomes in advanced public healthcare systems. Your work will directly impact how AI benefits individuals, businesses, and society at large.
 </li>
 <li>
 Engage with Leaders: interact directly with government officials in various countries and apply the first of its kind AI solutions while working alongside experienced ex. Founders, AI researchers, and software engineers to understand complex business challenges and deliver AI-powered solutions. Join a dynamic team where ideas are exchanged freely and creativity flourishes. You will be able to wear many hats: software building, product management, sales, interpersonal skills.
 </li>
 <li>
 Learn and Lead: Keep abreast of the latest developments in machine learning and AI. Participate in code reviews, share knowledge, and set an example with high-quality engineering practices.
 
 
 </li>
 </ul>
 
 You Might Thrive In This Role If You
 
 
 
 <ul>
 <li>
 Hold a BSc/Master&rsquo;s/PhD degree in Computer Science, Machine Learning, Data Science, or a related field.
 </li>
 <li>
 Have experience building GenAI-focused applications with the latest technologies, including but not limited to Agents, reasoning models and RAG.
 </li>
 <li>
 Have at least a high level familiarity with the architecture and operation of large language models.
 </li>
 <li>
 Have personally implemented models in common ML frameworks such as PyTorch, Jax or TensorFlow.
 </li>
 <li>
 Possess a strong foundation in data structures, algorithms, and software engineering principles.
 </li>
 <li>
 Exhibit excellent problem-solving and analytical skills, with a proactive approach to challenges.
 </li>
 <li>
 Can work collaboratively with cross-functional teams.
 </li>
 <li>
 Thrive in fast-paced environments where priorities or deadlines may compete.
 </li>
 <li>
 Are eager to own problems end-to-end and willing to acquire any necessary knowledge to get the job done.
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Competitive salary plus equity
 </li>
 <li>
 Daily lunches
 </li>
 <li>
 Commuter benefits
 </li>
 <li>
 401(k)
 </li>
 <li>
 Medical, Dental and Vision
 </li>
 <li>
 Unlimited PTO
 
 
 </li>
 </ul>
 
 Why Join Us
 
 
 
 <ul>
 <li>
 Ship quickly, iterate constantly and see your work deployed at global scale
 </li>
 <li>
 Collaborate with industry veterans from Tesla, DeepMind, Databricks, and more
 </li>
 <li>
 Accelerate your career with ownership based on impact, not tenure
 </li>
 <li>
 Earn competitive compensation + meaningful equity in a high-growth company
 </li>
 <li>
 Thrive in a culture built on speed, curiosity, and impact
 
 
 </li>
 </ul>
 
 If you want to see your work deployed at scale with real impact, Brain Co. is the place to build.
 
 
 
 
 
 This is a hybrid role that can be based in either our Abu Dhabi or Doha office
 
 
</div>

AI/ML Engineer, Deployed

Wynn Al Marjan Island

<div>
 
 Job Description
 
 
 
 
 About Wynn Al Marjan Island:
 
 
 
 On schedule to open in the United Arab Emirates in 2027, Wynn Al Marjan Island has been created as an opulent and entertaining beachside destination for discerning guests to play and relax. Located less than 50 minutes from Dubai International Airport, this integrated resort offering 1,530 rooms and suites, 22 restaurants and lounges, a theatre, nightclub, and five-star spa is currently under construction on a picturesque island that gracefully curves into the Arabian Gulf.
 
 
 
 About The Position
 
 
 
 Wynn Al Marjan Island is looking for a
 
 Data Scientist
 
 to join our
 
 Database Marketing
 
 team.
 
 
 
 Responsibilities
 
 
 
 The primary duties and responsibilities of this role are:
 
 
 <ul>
 <li>
 Build, maintain and optimize predictive models for the Gaming Marketing Campaigns
 </li>
 <li>
 Prepare datasets for modeling: cleaning, transforming, feature engineering, and validating data sources
 </li>
 <li>
 Maintain documentation for model inputs, business rules, and assumptions
 </li>
 <li>
 Support campaign operations by producing analytical variables, flags, segments, and model driven attributes
 </li>
 <li>
 Perform ad hoc analysis to support marketing decision making
 </li>
 <li>
 Build and maintain performance dashboards and recurring reports for Models and Campaigns performance
 </li>
 <li>
 Work with the Campaign Manager to ensure model outputs are correctly used during targeting and list creation
 </li>
 <li>
 Collaborate with Loyalty Marketing and other business units to understand their data needs and provide analytical support
 </li>
 <li>
 Assist in test and learn initiatives (eg., A/B tests, offer calibration) through proper setup and data preparation
 </li>
 <li>
 Perform checks to identify anomalies, missing data, or unexpected trends
 </li>
 <li>
 Keep analytical workflows organized, reproducible, and documented
 </li>
 <li>
 Summarize findings clearly for non-technical stakeholders
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 Master or Bachelor&rsquo;s degree in Data Science, Statistics, Mathematics, Computer Science, or a related analytical field.
 </li>
 <li>
 +2 years of experience in data science and analytics (Gaming experience is a plus but not required).
 </li>
 <li>
 Strong Python and SQL skills for data manipulation and analysis.
 </li>
 <li>
 Experience with Machine learning models (experience with Databricks is a plus)
 </li>
 <li>
 Solid understanding of basic statistical concepts (regression, segmentation, clustering, sampling, validation)
 </li>
 <li>
 Experience working with large datasets and operational, recurring analytical tasks.
 </li>
 <li>
 High attention to detail with strong QA instincts.
 </li>
 <li>
 Ability to explain analytical outputs clearly to non‑technical stakeholders.
 </li>
 <li>
 Organized, reliable, and willing to handle operational and routine tasks as part of learning the gaming analytics environment.
 
 
 </li>
 </ul>
 
 About Wynn Al Marjan Island&rsquo;s Benefits
 
 
 
 We offer an attractive salary, paid in Dirhams (AED), the local currency of the UAE. In addition, we offer an excellent leave policy, a healthcare package, incentive programs, and other employee benefits . The result is a package that makes this role highly attractive to outstanding applicant s seeking a career with Wynn Resorts, among the most renowned and celebrated brands in the global hospitality industry.
</div>

Data Scientist - Database Marketing

BigData Technology Solutions

Datacenter Facility Engineer L1 Location: Abu DhabiJob SummaryThe position Datacentre Facility engineer is responsible for high-quality IT service delivery to customers in alignment with agreed SLAs, KPIs, and contractual commitments. The role oversees datacenter facility management and support across all managed facilities------------------------------------------------------------------------------------------Key Education and Technical Qualification,<ul><li>Bachelor&rsquo;s/ Master&rsquo;s degree in IT / science discipline</li><li>ITIL Foundation V4.0 or 3.0- Mandatory</li></ul>-------------------------------------------------------------------------------------------Key Competencies<ul><li>3+ years of experience in data center operations</li><li>Clear understanding of data center infrastructure</li><li>Cable management &amp; rack organization</li><li>Basic electrical knowledge</li><li>Data Center deep cleaning experience</li><li>Knowledge on passive material (Cables, Pannels, Infrastructure equipment&rsquo;s, software etc)</li><li>Data Center Power Systems</li><li>UPS &amp; Generator Operations</li><li>Cooling Systems (CRAC/CRAH)</li><li>DCIM Tools</li><li>BMS/EMS Monitoring</li><li>Working knowledge of ITIL Processes</li><li>Electrical Safety Awareness</li></ul>-------------------------------------------------------------------------------------------Key Responsibilities<ul><li>Operations Management</li><li>Critical Facilities Management</li><li>Incident &amp; Problem Management</li><li>Change Management</li><li>Vendor Management</li><li>Compliance &amp; Governance</li><li>Capacity &amp; Availability Management</li></ul>-------------------------------------------------------------------------------------------Application Question(s):<ul><li>What is your notice period to join?</li><li>What is your expected salary in AED?</li><li>Are you in UAE, to start Immediately?</li><li>How many years of work experience do you have in data center operations ?</li></ul>Work Location: In person

Datacenter facility engineer L1

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