Data Engineer

Description
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
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