Government-backed Abu Dhabi organization focused on advanced technology R&D (est. 2020), defining strategy, funding, and policies across AI, robotics, and emerging technologies. Oversees the full innovation lifecycle - from research and programs to commercialization - through dedicated applied research, innovation, and venture entities.

The first production system is an AI-enabled operational platform that gives a senior leadership team a shared situational picture, an AI-classified signal feed, a daily AI-generated briefing, and an action accountability tracker. MVP target: operational within two weeks of team formation. The platform is also the technical foundation for all subsequent Data & AI systems across the organization.

Build and operate the data platform that powers the DAIO's (Data & AI Office) production systems and the long-term data estate. In the immediate term: the signal ingestion pipeline, data quality layer, and observability for all data flows. In the medium term: the enterprise data warehouse on Azure and sovereign compute, the metadata catalog, and the governed data access layer for AI agents.

WHAT THIS ROLE BUILDS & OWNS

• Signal ingestion pipeline - 30-minute polling job across all defined open-source feeds (news wires, maritime AIS, financial feeds, social/keyword feeds)

• Deduplication and normalization layer - common signal schema across all sources

• Ingestion observability - every item logged with source, timestamp, processing status, and failure reason; no silent drops

• PostgreSQL schema deployment and migration scripts (Alembic)

• Azure Redis Cache - session management and ingestion queue configuration

• Phase 2 data warehouse: ADLS + Synapse/Fabric, data ingestion from SAP, M365, and ATRC enterprise systems

• Data quality monitoring - automated checks on signal completeness, classification coverage, and freshness

KEY DECISIONS THIS ROLE OWNS

• Polling frequency, retry logic, and backoff strategy for each signal source

• Deduplication key design - what makes a signal unique across sources

• Whether a data quality failure is a warning (flag it) or a stop (pause ingestion)

• Schema migration approach - blue-green, Alembic auto-migrate, or manual rollout

• Data retention schedule - what is archived, what is purged, and when

WHAT THIS ROLE DOES NOT DO

• Define the data model or classification schema - that is the Head of Data Architecture

• Build the application API endpoints - that is the Backend/Systems Engineers

• Write AI prompts or tune classification outputs

• Manage cloud infrastructure provisioning - that is a DevOps/infra function

• Microsoft data stack - Fabric or Synapse;

• Strong Python(custom integration code, async processing);

• Building and supporting data pipelines(Airflow or similar);

• Postgres/Alembic

• Observability and monitoring(prometheus/grafana/etc)