<p>We are looking for a Data Platform Engineer to join a 4-month contract opportunity based in Norman, Oklahoma. This role owns the storage, retention and pipeline-correctness workstream. This is data engineering, not cloud </p><p>infrastructure.</p><p><br></p><p>What they will actually do</p><p>▪ Own the storage and pipeline backlog across the ingestion and processing repositories</p><p>▪ Event ordering and bitemporal modelling: separating event time from ingestion time, deterministic replay</p><p>▪ Atomic snapshot publication with schema admission and rollback</p><p>▪ Design the historical layer on object storage so it can be queried economically and answer as-of </p><p>questions correctly — file format, partitioning, compaction, and whether the canonical layer moves </p><p>onto a managed table format</p><p>▪ Reconcile the operational database and the historical store so a point-in-time question returns one </p><p>answer rather than two</p><p>▪ Evidence immutability and retention: object-lock semantics, preventing overwrite of stored evidence, </p><p>validated recovery paths</p><p>▪ Pipeline durability: idempotent runs, completeness receipts, restore testing</p><p>▪ Support production cutover in month 4 — this seat does not roll off early</p>

<p>Required</p><p>▪ 5+ years data engineering</p><p>▪ Python and SQL to a strong standard</p><p>▪ Practical experience with event ordering, idempotency and exactly-once or effectively-once </p><p>publication</p><p>▪ Bitemporal or as-of data modelling — this is the single most important requirement</p><p>▪ Open table formats in production — Apache Iceberg, Delta Lake or Hudi. Not the name on a </p><p>CV: partitioning strategy, compaction, snapshot isolation, schema evolution and time travel, and the </p><p>judgement about when a table format earns its complexity</p><p>▪ Object-storage query engines — Athena, Trino, Spark SQL or equivalent — including why a query that</p><p>was fast last quarter is slow now</p><p>▪ AWS data services: S3, RDS/Postgres, DynamoDB, Glue Data Catalog</p><p>▪ Uses AI coding tooling as part of their normal workflow</p><p><br></p><p>Useful but not required</p><p>▪ Managed Iceberg services (S3 Tables) or self-managed Iceberg on object storage</p><p>▪ dbt, Snowflake, Databricks or similar warehouse tooling — and a view on when a warehouse is and is </p><p>not worth it</p><p>▪ Geospatial data (geometry, multipart and hole semantics)</p><p>▪ Content-addressed storage or evidence-preservation systems</p><p><br></p>

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