Data Engineer, Unified Platform

Work closely with Data Strategists to determine appropriate data sources and implement processes to onboard and manage new data sources for trading, research, and back-office purposes.

Contribute to data governance processes that enable discovery, cost-sharing, usage tracking, access controls, and quality control of datasets to address the needs of DRW trading teams and strategies.

Continually monitor data ingestion pipelines and data quality to ensure stability, reliability, and quality of the data. Contribute to the monitoring and quality control software and processes.

Own the technical aspects of vendor ingestion pipelines, coordinating with vendor relationship managers on upcoming changes, performing routine data operations without breaking internal users, and contributing to the team's on-call rotation to respond to unanticipated changes.

Rapidly respond to user requests, identifying platform gaps and self-service opportunities that make the user experience more efficient.

Have experience designing and building data pipelines

Have experience working within modern batch or streaming data ecosystems

An expert in SQL and have experience in Java or Python

Can apply data modeling techniques

Able to own the delivery of data products, working with analysts and stakeholders to understand requirements and implement solutions

Able to contribute to project management and project reporting, * 3+ years of experience working with modern data technologies and/or building data-first products.

Excellent written and verbal communication skills.

Proven ability to work in a collaborative, agile, and fast-paced environment, prioritizing multiple tasks and projects, and efficiently handle the demands of a trading environment.

Proven ability to deliver rapid results within processes that span multiple stakeholders.

Strong technical problem-solving skills.

Extensive familiarity with SQL and Java or Python, with a proven ability to develop and deliver maintainable data tranformations for production data pipelines.

Experience leveraging data modeling techniques and ability to articulate the trade-offs of different approaches.

Experience with one or more data processing technologies (e.g. Flink, Spark, Polars, Dask, etc.)

Experience with multiple data storage technologies (e.g. S3, RDBMS, NoSQL, Delta/Iceberg, Cassandra, Clickhouse, Kafka, etc.) and knowledge of their associated trade-offs.

Experience with multiple data formats and serialization systems (e.g. Arrow, Parquet, Protobuf/gRPC, Avro, Thrift, JSON, etc.)

Experience managing data pipeline orchestration systems (e.g. Kubernetes, Argo Workflows, Airflow, Prefect, Dagster, etc.)

Proven experience in managing the operational aspects of large data pipelines such as backfilling datasets, rerunning batch jobs, and handling dead-letter queues.

Prior experience triaging data quality control processes, correcting data gaps and inaccuracies.