Staff & Senior Software Engineers - Data Platforms
Job Description
[c. $200k-$300k Base Salary + Equity (or equivalent) | Remote Working - East Coast US & Europe Only]
Role Overview
We’re representing a remote-first financial data technology company expanding the engineering organisation behind a large-scale data platform used by institutional and sophisticated technical users. Its systems ingest, transform and serve extremely large datasets, with engineering decisions directly affecting data quality, availability, latency and infrastructure cost. Approximately three engineers are being hired across the broader data organisation. The current plan combines a Staff-level Data Foundation appointment with two further Senior hires spanning foundation, curated datasets and related data-product work. Exact team allocation retains some flexibility as the organisation evolves.
These are software-engineering roles in a data-intensive environment - not conventional analytics or pipeline-only positions. Engineers will remain hands-on while owning production architecture, solving distributed-systems problems and improving how data moves from ingestion through processing, curation and external delivery...
Role Snapshot
- Staff - Data Foundation: lead architecture across core ingestion, representation and delivery systems, translating loosely defined product needs into interfaces, system boundaries and sequenced engineering work
- Senior / Staff - Curated Data: build the software layer governing complex dataset lifecycles, including orchestration, dependencies, backfills, schema changes, retries, recovery and data-quality controls
- Senior - Data Foundation / Platform: own production components around ingestion, decoding, distributed processing, large-scale storage and query infrastructure, with final team placement influenced by strengths and organisational priorities
- These are Senior and Staff individual-contributor appointments where level is determined by engineering scope and judgement rather than a prescribed number of years
- Bring strong computer-science and backend-engineering foundations, with the ability to reason about distributed state, failure modes, system contracts, performance and scalability rather than relying on familiar frameworks
- Work across a polyglot environment centred on Go, Rust and JVM technologies, with Python and SQL where appropriate; strength in another language is viable where the underlying engineering fundamentals are strong
- Expect petabyte-scale data challenges where memory consumption, compute efficiency, latency and operational behaviour matter alongside correctness
- Depending on the team, relevant depth may include Kafka or other streaming systems, orchestration/scheduling, stateful processing, schema evolution, SQL-heavy data modelling, Parquet/Iceberg-style storage or tooling such as dbt and SQLMesh
- Take responsibility for production behaviour - diagnosing incidents, identifying recurring failure patterns, improving observability and reliability, and implementing fixes that remove underlying causes rather than symptoms
- (Preferred) Experience building data products, financial or market-data platforms, blockchain infrastructure or similarly critical data systems is useful, but adaptability, strong engineering judgement and production ownership matter more than prior sector knowledge
...
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