Senior Data Engineer
New
K
KeyrockDigital Assets
Brussels, Rome, Barcelona, Zug, Porto, Cape Town, Istanbul, Eindhoven, Geneva, Milan, Amsterdam, Manama, Madrid, London, Dubai, Berlin, Antwerp, Copenhagen, Vienna , Prague, Athens, Budapest, Paris, Frankfurt, Dublin, Warsaw, Sofia, Luxembourg, Remote, Zurich, Abu Dhabi, Lisbon, London, Tallinn, London OR Brussels, Seychelles, Rotterdam, Krakow, Johannesburg, Zagreb, business-hours on-call shared across the teamFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- DockerPythonSQLApache KafkaClickhouseData engineeringCI/CDTerraformData modeling
Requirements
- 8+ years of experience building production data systems.
- Strong proficiency in Python and SQL.
- Deep understanding of data modeling for streaming and analytical workloads.
- Experience designing/operating streaming systems (Kafka, Redpanda, MSK, or Kinesis).
- Practical experience with time-series databases (e.g., ClickHouse, TimescaleDB, QuestDB).
- Familiarity with lakehouse architecture, table partitioning, and compaction.
- Strong operational skills with Docker, Terraform, and CI/CD workflows.
- Ability to design for self-healing, idempotency, and high data quality.
- Understanding of financial market data such as order books, trades, and portfolios.
- Excellent communication and collaboration skills with technical and business stakeholders.
- Interest in or experience with Rust is a plus.
Responsibilities
- Build streaming and batch pipelines that ingest, normalize, and distribute market, trading, and portfolio data.
- Develop self-serve tooling including SDKs, patterns, templates, and AI agents for data consumers.
- Manage data contracts and schema evolution to reduce multi-team coordination.
- Design lakehouse and time-series layers tailored to query patterns.
- Implement data governance and quality frameworks covering schema validation, lineage, and stale-feed detection.
- Create derived analytics for desks and portfolio management, such as VWAP, order book microstructure, and exposure metrics.
- Ensure observability, cost-efficiency, and high performance from inception.
- Utilize infrastructure as code (Docker, Terraform, CI/CD) in partnership with the infrastructure team.
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