This is a rare opportunity to apply serious data engineering in a domain where latency, correctness, and reliability carry direct commercial weight. * Requirements * 6+ years data engineering in production environments; Python expertise – idiomatic, well-tested, production-grade code, not notebook scripts * ETL/ELT pipeline design and implementation at scale; orchestration with Airflow, Prefect, or equivalent; reliability-first mindset including backfill, retry, and exactly-once semantics * Azure data platform – Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage; infrastructure as code for data workloads (Terraform or Bicep) * Databricks – Delta Lake, Unity Catalog, job cluster vs interactive cluster trade-offs, cost-aware compute management, Spark job optimisation * Relational databases: PostgreSQL at production scale – query optimisation, indexing strategies, table partitioning, replication, schema design for both OLTP and analytical workloads * MongoDB – document modelling, aggregation pipelines, indexing strategy, replica sets; clear judgment on when document vs relational storage is the right architectural call * Containerisation: Docker and Kubernetes-based deployment of data workloads; reproducible, environment-agnostic data infrastructure * Data modelling for analytical workloads – dimensional modelling, data vault, or equivalent; schema evolution, slowly changing dimensions, and downstream impact analysis * Stream and batch processing patterns; late data handling, watermarking, and backfill strategies; throughput vs latency trade-offs in pipeline design * Production data observability – data lineage, quality checks, SLA monitoring, alerting on freshness and completeness; treating data correctness as a first-class concern * CI/CD for data infrastructure – version-controlled pipelines, automated data quality testing, reproducible and auditable deploys * Ability to work directly with quant researchers, risk managers, and traders – translate business requirements into reliable, well-documented data products * Nice to Have * Financial markets data – market data feeds (Bloomberg, Refinitiv), tick data, trade history, reference data, or instrument master management * Apache Spark or Flink for large-scale stream and batch processing beyond the Databricks ecosystem * dbt or equivalent SQL transformation layer; experience building and maintaining dbt projects in a production data warehouse * Event streaming with Kafka or Confluent Platform – topic design, consumer group management, exactly-once delivery guarantees * OLAP-optimised stores – ClickHouse, DuckDB, or equivalent; understanding of columnar storage and vectorised query execution * Energy, commodities, or broader financial markets domain knowledge * What We’re Looking For * You treat data as a product, not a side effect. You know what it takes to make a pipeline trustworthy – not just running, but observable, tested, and recoverable when something upstream changes at 3am. You think in systems: schema evolution, lineage, freshness SLAs, and the downstream impact of every modelling decision. At ETrading , that data is the foundation of billion-dollar trading decisions. You are the reason it is right.
To find out more about Huxley, please visit (url removed)
Huxley, a trading division of SThree Partnership LLP is acting as an Employment Business in relation to this vacancy | Registered office | 8 Bishopsgate, London, EC2N 4BQ, United Kingdom | Partnership Number | OC(phone number removed) England and Wales
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