Senior Data Engineer

Role: Senior Data Engineer

Location: Abu Dhabi

About Presight

Presight is an ADX-listed public company with Abu Dhabi based G42 as its majority shareholder and is the region’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:

We are seeking a Senior Data Engineer to own end-to-end data delivery – from source-system analysis and data architecture through data modelling, pipeline engineering, and analytics enablement. You will define the target-state architecture, model enterprise data, engineer reliable end-to-end pipelines, and turn complex datasets into trusted, decision-ready insight. The role blends strong data engineering with data architecture leadership and the analytical curiosity of a data analyst, operating across a multicultural, multinational environment with occasional travel to customer sites.

Responsibilities

Key Responsibilities:

Functional

• Own the complete data lifecycle – requirements, sourcing, architecture, modeling, pipeline engineering, quality, and delivery of analytics-ready data.
• Translate business and information needs into delivery roadmaps, then design, build, and operate the solutions that meet them.
• Define the target-state data architecture, infrastructure, and interfaces to data sources, including tooling for automated data loads.
• Design lake / warehouse / lakehouse architecture spanning ingestion, storage, processing, serving, and consumption layers, embedding security, access control, privacy, and compliance considerations.
• Analyze source systems to understand data semantics and develop conceptual, logical, and physical data models, defining granularity and identifying facts and dimensions to design dimensional models (star and snowflake schemas) from business requirements.
• Execute the full model lifecycle – conceptual through physical – applying normalization, denormalization, and modern modeling patterns appropriate to each use case.
• Design, develop, and maintain scalable batch and real-time/streaming data pipelines that ingest from databases, applications, files, and event streams.
• Build and operate orchestration, scheduling, and automated data-load frameworks with robust logging, alerting, retry, and monitoring.
• Optimize data processing and distributed compute, tuning for performance, cost, and scalability.
• Engineer data cleansing and integration solutions; develop plans to automate clean-up using existing or new capabilities.
• Map data sources and targets for data movement, ensuring end-to-end data integrity, lineage, and quality.
• Apply software-engineering best practices – version control, code review, automated testing, and continuous integration and delivery for data assets.
• Implement data quality checks, validation rules, reconciliation, and observability across the delivery pipeline.
• Review data to identify anomalies that hinder evolution to the future-state architecture, and define remediation roadmaps.
• Establish and govern enterprise data architecture and modeling standards, naming conventions, reference patterns, documentation, data dictionaries, and metadata to keep assets discoverable and consistent.
• Support modernization of existing data integration assets and migration of legacy solutions to the target platform.
• Partner with IT and data owners to catalog data across systems and align it to the target-state architecture.
• Interpret data using statistical techniques; identify and interpret trends and patterns in complex datasets to surface actionable insight and provide ongoing reports and dashboards to stakeholders.
• Look for the “why” behind the numbers – ask questions, investigate root causes, and define new process-improvement opportunities.
• Build and maintain data visualizations and self-service reporting; profile data, detect anomalies, and validate quality to support confident, data-driven decisions.
• Work with management to prioritize business and information needs and translate them into technical solutions, collaborating with IT and data owners across diverse data sources and platforms.
• Communicate clearly and consistently within culturally diverse, multinational teams; operate with flexibility and resilience, handle ambiguity, and travel to customer locations as required by the business.
• 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.

Requirements:

Experience & Education

• Minimum 10 years of relevant experience across data engineering, data architecture, data modeling, and data analysis.
• Bachelor’s degree in Information Technology, Computer Science, or a related field.
• Proven working experience as a data analyst with strong command of statistical methods and data visualization.
• Demonstrated delivery of enterprise-scale data pipelines and platforms in production.
• MANDATORY: Hands-on experience with the Microsoft Azure data stack (e.g., Azure Data Factory, Azure Databricks / Synapse, ADLS Gen2, Azure SQL) is mandatory for this role.

• Advanced data querying and manipulation, including complex logic, analytical functions, and performance tuning.
• Proficiency in a data engineering / data analysis programming language.
• Hands-on experience across the full data-model lifecycle using industry-standard modeling tools.
• Strong expertise defining granularity and identifying facts and dimensions for star and snowflake schemas based on business requirements.
• ETL/ELT design, data integration, workflow orchestration, and data-cleansing automation.
• Distributed processing and large-scale (big data) handling.
• Data warehousing and data lake / lakehouse storage architecture.
• Knowledge of statistics and experience using statistical methods to analyze datasets.
• Business intelligence and data visualization for self-service reporting.
• Cloud data platform experience across ingestion, storage, processing, and serving.
• Familiarity with version control, continuous integration and delivery, and data observability / quality practices.

• Excellent problem-solving and communication skills.
• Self-driven, motivated, results-oriented, with a customer-centric mindset.
• Curious and analytical – consistently looking for the “why” behind the data.
• Resilient and comfortable with ambiguity; flexible and open to travel as required.
• Collaborative team player who thrives in a multicultural, multinational environment.

If you are a performance-driven, inquisitive mind with the agility to adapt to ambiguity, you will fit right in. You should be eager to explore opportunities to build meaningful collaborations with stakeholders and aspire to create unique customer-centric solutions. Bias for action and a passion to conquer new frontiers in the AI space is at the heart of the Presight community.

What working at Presight offers:

Culture: An open, diverse and inclusive environment with a global vision that encourages personal growth and focuses on ground-breaking, industry-first innovations.

Career: Accelerate your career through high-impact projects and access to resources for continuous growth and learning opportunities.

Rewards: A competitive remuneration package with a host of perks including healthcare, education support, leave benefits and more.