Senior Data Engineer

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.

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.

• 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.
• Partne…