Lead Data Engineer - Telemetry Systems Analytics

About Us

STARK is a new kind of defence technology company revolutionising the way autonomous systems are deployed across multiple domains. We design, develop and manufacture high performance unmanned systems that are software-defined, mass-scalable, and cost effective. This provides our operators with a decisive edge in highly contested environments.

We’re focused on delivering deployable, high-performance systems—not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe - today.

• Strong background in data engineering, telemetry systems, or large-scale data pipelines in aerospace, UAVs, robotics, or similar high-complexity hardware environments.
• Deep experience with ingestion, ETL, and time-series/streaming data (e.g., Kafka, Influx
  
  DB, Parquet, S3-based lakes).
• Experience building dashboards for engineering organizations (Grafana, Superset, internal tools).
• Understanding of flight test environments, avionics data formats, sensor fusion pipelines, PX4/Ardupilot or proprietary telemetry protocols.
• Ability to design data structures that support qualification and certification evidence.
• Strong systems and software fundamentals; ability to work hands-on.
• Security-minded; familiar with export control and defence-sector data constraints (plus onboarding ability for our compliance stack).
• Proven leadership: builds processes, aligns stakeholders, drives execution.

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As the Lead Data Engineer you will own the full data lifecycle at Stark Defence across telemetry, testing, flight operations, qualification, and certification. Building a robust data function that turns raw system output into reliable, decision-ready information for engineering, flight test, and program leadership.

• Capture, ingest, and structure all telemetry and test data across air, ground, and simulation environments.
• Develop a standardized data pipeline covering test runs, flight runs, and qualification events.
• Build dashboards and analytical tools that give real-time and historical visibility into system performance, regressions, and anomalies.
• Ensure data quality, traceability, and audit readiness for future qualification and certification work.
• Lead data strategy for long-term fleet operations (scalability, streaming, storage, schema evolution).
• Partner with engineering teams to define metrics, interfaces, APIs, and data formats.
• Establish governance around secure handling of sensitive and export-controlled data.
• Build and mentor a small data team over time (data engineering, analytics, possibly ML/controls interface).