Data Engineer

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 

infrastructure.

What they will actually do

▪ Own the storage and pipeline backlog across the ingestion and processing repositories

▪ Event ordering and bitemporal modelling: separating event time from ingestion time, deterministic replay

▪ Atomic snapshot publication with schema admission and rollback

▪ Design the historical layer on object storage so it can be queried economically and answer as-of 

questions correctly — file format, partitioning, compaction, and whether the canonical layer moves 

onto a managed table format

▪ Reconcile the operational database and the historical store so a point-in-time question returns one 

answer rather than two

▪ Evidence immutability and retention: object-lock semantics, preventing overwrite of stored evidence, 

validated recovery paths

▪ Pipeline durability: idempotent runs, completeness receipts, restore testing

▪ Support production cutover in month 4 — this seat does not roll off early

Requirements

Required

▪ 5+ years data engineering

▪ Python and SQL to a strong standard

▪ Practical experience with event ordering, idempotency and exactly-once or effectively-once 

publication

▪ Bitemporal or as-of data modelling — this is the single most important requirement

▪ Open table formats in production — Apache Iceberg, Delta Lake or Hudi. Not the name on a 

CV: partitioning strategy, compaction, snapshot isolation, schema evolution and time travel, and the 

judgement about when a table format earns its complexity

▪ Object-storage query engines — Athena, Trino, Spark SQL or equivalent — including why a query that

was fast last quarter is slow now

▪ AWS data services: S3, RDS/Postgres, DynamoDB, Glue Data Catalog

▪ Uses AI coding tooling as part of their normal workflow

Useful but not required

▪ Managed Iceberg services (S3 Tables) or self-managed Iceberg on object storage

▪ dbt, Snowflake, Databricks or similar warehouse tooling — and a view on when a warehouse is and is 

not worth it

▪ Geospatial data (geometry, multipart and hole semantics)

▪ Content-addressed storage or evidence-preservation systems