[Remote] Senior Data Engineer

Note: The job is a remote job and is open to candidates in USA. Keyrock is a leading change-maker in the digital asset space, known for its innovative approach and diverse team. They are seeking a Senior Data Engineer to help build the Keyrock Data Platform, enabling various teams to access and utilize data efficiently for trading and asset management purposes.

Responsibilities

• Build streaming and batch pipelines that ingest, normalise, and distribute market, trading, and portfolio data, resilient to feed and exchange failures
• Build the self-serve tooling (SDKs, patterns, templates, AI agents) so other teams publish, consume, and build on data products without waiting on us
• Own data contracts and schema evolution. Keep schema changes from turning into multi-team coordination events
• Design the lakehouse and time-series layer around consumer query patterns
• Build and evolve the Data Governance and Data Quality Framework: stale-feed detection, schema validation, range checks, idempotent writes, lineage, ownership, self-healing
• Build the derived analytics the business runs on: cross-exchange spreads, VWAP at depth, order book microstructure for the desks; portfolio views, exposure, performance for wealth and asset management
• Make observability, cost, and performance first-class from day one
• Treat infrastructure as code (Docker, Terraform, CI/CD) alongside our Central Infrastructure Team
• Work in the open: write things down, partner closely with Architecture, Infrastructure, Platform, and the rest of the teams

Skills

• 8+ years of building production data systems that other people rely on
• Strong proficiency in Python and SQL: not just being able to write a query, but being able to reason about what the engine is doing with it
• Code that's easy for someone else to read, test, and delete later
• Strong understanding of data modelling for both streaming and analytical workloads
• Efficiency, quality, idempotency, and observability are taken seriously by default
• You've designed and operated streaming systems on Kafka, Redpanda, MSK, or Kinesis, and you have opinions about partitioning, consumer groups, offsets, and schema registries
• You've used a time-series store in production (ClickHouse ideally; TimescaleDB, QuestDB, or similar are fine too) and can talk about table design as a function of query patterns
• You've worked with a lakehouse architecture and reason about table layout, partitioning, and compaction as design choices that shape query performance and storage cost
• You build for self-healing and idempotency. Reprocessing is safe, retries don't double-write, and the system recovers without a human in the loop
• Docker, Terraform, and CI/CD are how you work, not a separate 'DevOps' thing
• You think about cost and performance early
• You instrument as you build: logs, metrics, and traces are part of the system from day one
• You design for data quality and governance up front covering contracts, validation, lineage, and ownership
• You reason from first principles when a problem is new, stay pragmatic when it isn't, and update your view when you learn more
• You treat the trading desks, wealth and asset management, product, risk, finance, compliance, and research as customers of what you build, and communicate with them that way
• You optimise for outcomes over output. A smaller, simpler thing that ships and works beats a bigger thing that doesn't
• You take ownership end-to-end: design, ship, operate, improve
• You say what you think including when it's an unpopular take. You change your mind when the argument is better
• You make the people around you better. Reviews are real, juniors grow from working with you, and peers want to work with you again
• You're curious about how markets work. Data engineering on its own won't keep you interested here
• You're honest about what you know and what you don't, and quick to close the gap
• You understand financial market data: order books, trades, reference data, portfolios, exposures. Crypto, TradFi, or both are a strong plus
• Lakehouse experience with Apache Iceberg or Delta Lake
• Familiarity with DataHub or similar metadata/lineage platforms
• Rust. Some of our performance-critical services are written in it. Interest is welcome; fluency isn't required

Benefits

• A competitive salary package, with various benefits.
• Flexible hours, remote-first, business-hours on-call shared across the team.
• Regular online get-togethers and a yearly onsite where everyone's in the same room.

Company Overview