Senior Data Engineer

Keyrock; Eindhoven; Hybrid; EUR 85,000 - 120,000; Full time

Job summary

Keyrock is expanding its Central Data Team and seeks a senior data platform engineer to build streaming and batch pipelines, govern data contracts, and design a scalable lakehouse. You’ll enable multiple business units including trading desks and asset management with reliable, observable data services.

You will own self-serve data tooling, champion data quality, and drive cost-aware, scalable data infrastructure in a predominantly remote, globally distributed environment.

Qualifications

• 8+ years building production data systems
• Strong proficiency in Python and SQL
• Code that is readable, testable, and maintainable
• Solid understanding of data modelling for streaming and analytical workloads
• Emphasis on efficiency, quality, idempotency, and observability

Responsibilities

• Build streaming and batch pipelines ingesting, normalising, distributing market and portfolio data
• Create self-serve tooling (SDKs, patterns, templates, AI agents) for data product teams
• Own data contracts and schema evolution to avoid cross-team coordination issues
• Design lakehouse and time-series layer around common query patterns
• Develop Data Governance and Data Quality Framework (stale-feed detection, validation, lineage)
• Build derived analytics (cross-exchange spreads, VWAP, desk-level insights)
• Prioritise observability, cost control and performance from day one
• Treat infrastructure as code with Docker, Terraform, CI/CD
• Collaborate openly with Architecture, Infrastructure, Platform teams

Skills

Python

SQL

Data modelling

Observability

CI/CD

Tools

Kafka/Redpanda/MSK/Kinesis

ClickHouse/TimescaleDB/QuestDB

Docker

Terraform

Job description

About Keyrock

Since our beginnings in 2017, we've grown to be a leading change-maker in the digital asset space, renowned for our partnerships and innovation.Today, we partner with over 250 team members around the world. Our diverse team hails from 42 nationalities, with backgrounds ranging from self-taught DeFi natives to PhDs. Predominantly remote, we have hubs in London, Brussels, and Singapore, and host regular online and offline hangouts to keep the crew tight.We are trading on more than 80 trading venues, and working with a wide array of asset issuers. As a well-established market maker, our distinctive expertise led us to expand rapidly. Today, our services span market making, options trading, high-frequency trading, OTC, and DeFi trading desks.But we’re more than a service provider. We’re an initiator. We’re pioneers in adopting the Rust Development language for our algorithmic trading, and champions of its use in the industry. We support the growth of Web3 startups through our Accelerator Program. We upgrade ecosystems by injecting liquidity into promising DeFi, RWA, and NFT protocols. And we push the industry's progress with our research and governance initiatives.At Keyrock, we're not just envisioning the future of digital assets. We're actively building it.

About The Team And Why The Role Exists

The Central Data Team (CDT) is only a few months old, but data has been Keyrock's lifeblood since day one. We're now building the Keyrock Data Platform to give Keyrockers, and the AI agents working alongside them, the data and context they need to act fast and autonomously within agreed boundaries and aligned with our shared goals. Doing that means taking data from across the company and making sense of it in real time for all the functions that depend on it: trading desks, wealth and asset management, product, risk, finance, compliance, and research to name a few.You'd be one of the early hires in CDT and we expect you to contribute to most of the bigger decisions and build work. The standards we settle on and the way the team ends up working are still open.

What You’ll Do

• 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.

What We’re Looking For

Tools matter less to us than how you think about problems. That said, here's the shape of the person we have in mind.

Engineering Craft

• 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.

Systems Design

• 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.

Operational Readiness

• 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