Data EngineerRobert Half

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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