Jerome Hardaway.

For teams hiring a Senior Data Engineer

I build pipelines where every number names its source.

I turn messy public and commercial data into records people make career decisions on. Each stage writes output a person can read, and each figure says where it came from.

Résumé Contact

What you get

  • 4,455military job codes parsed and indexed across five service branches
  • 8keyed datasets produced by one staged pipeline
  • 3 tiersof salary data, with the tier recorded on every row
  • $0in API keys for the O*NET and BLS scoring chain

The three artifacts to read

  • MOS Translator

    A staged, resumable pipeline: parse job codes, generate schema-validated records, join them to a role taxonomy and salary data, publish static pages and a gap report.

  • AI-Era Job Kit

    Ingests O*NET database files, BLS tables and the BLS API, and public job board APIs with the standard library alone. It stops when a source is unavailable.

  • J0DI3 AI backend

    PostgreSQL with pgvector as the system of record, migrations tested down and back up in CI, field-level encryption, access audit logging and 30-day retention.

How I work with data

  • Label the source on the row. Salary records carry a dataSource field, so a reader can tell a live Lightcast figure from a Census estimate or a curated value.
  • Validate at the boundary. Generated records must match a schema before they are written. A quote that is not in the source text is rejected.
  • Refuse stale data. Postings older than 14 days and O*NET data older than 30 are rejected, and scripts stop when a live source is down.
  • One definition per number. Each published outcome statistic is defined once, with its source and date, and pinned by a test.
  • Make runs resumable. Batches are small and merge into existing output, so a failed run continues where it stopped.

The reasoning behind each of these is in the decision records.

Data experience beyond these artifacts

  • Senior Data Engineer, Serket-Tech Cyber Security. Designed the risk taxonomy and star schema data model behind an enterprise GRC Risk Register for a Fortune 500 healthcare company, and delivered Power BI dashboards with real-time filtering and drill-through.
  • Vets Who Code. Firebase and GCP engagement data pipelines on Apache Kafka and Spark.
  • Microsoft. React and Python BI dashboards for Starbucks that cut retrieval time by about 50%.

Dates and detail are on the résumé.

Skills

  • Python
  • SQL
  • Pandas
  • NumPy
  • Data pipelines and migrations
  • Apache Kafka
  • Apache Spark
  • Star schema modeling
  • Power BI
  • PostgreSQL
  • pgvector
  • Database performance
  • TypeScript
  • CI/CD
  • Terraform
  • Google Cloud Run

Logistics

I am based in Atlanta, Georgia, and work remotely. I am open to Senior Data Engineer roles, especially on teams building data for AI products.