Senior Engineer, Data Engineering

Keyrock
Barcelona, Spain
11 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience required
8 years minimum
Working hours
Regular working hours

Tech stack

Query Performance Artificial Intelligence Algorithmic Trading Continuous Integration Data Architecture Information Engineering Data Governance Data Infrastructure DevOps Python (Programming Language) Operational Databases SQL Databases
+7 more
Data Streaming Apache Kafka Web3.js Vertica Terraform Docker Programming Languages

Job description

Since our beginnings in **, we’ve grown to be a leading change-maker in the digital asset space, renowned for our partnerships and innovation.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’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.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.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.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.Build and evolve the Data Governance and Data Quality Framework: stale-feed detection, schema validation, range checks, idempotent writes, lineage, ownership, self-healing.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.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; 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.Docker, Terraform, and CI/CD are how you work, not a separate “DevOps” thing.You think about cost and performance early.You design for data quality and governance up front #

Requirements

Treat infrastructure as code (Docker, Terraform, CI/CD) alongside our Central Infrastructure Team. 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; 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. Docker, Terraform, and CI/CD are how you work, not a separate “DevOps” thing. You think about cost and performance early. You design for data quality and governance up front #

About the company

Barcelona, España

Since our beginnings in **, we’ve grown to be a leading change-maker in the digital asset space, renowned for our partnerships and innovation.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’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. 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. 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.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. Build and evolve the Data Governance and Data Quality Framework: stale-feed detection, schema validation, range checks, idempotent writes, lineage, ownership, self-healing.

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