Senior Data Engineer

Join Omaha Insights, a rapidly expanding financial data and analytics company. As a Senior Data Engineer, you will own the platform end to end, leading the migration to Databricks and PySpark, building and running data pipelines, and supporting AI and document processing. You will have the opportunity to define how the team works, lead hiring for future data engineering roles, and make a direct impact on the company.

Missions

• Mener la migration de la plateforme actuelle vers Databricks et PySpark, en définissant l'architecture et en supervisant la transition.
• Construire et gérer les pipelines de données qui produisent les ensembles de données financières, en optimisant les performances et la fiabilité.
• Définir les normes de travail de l'équipe, y compris la révision du code, les tests, le déploiement et la documentation.

Profil recherché

You've owned a data platform, not just contributed to one. You're happy being the person accountable when it breaks, and you'd rather build the thing than argue about it in a committee.

Must have

• 8+ years building production data systems, including at least one platform you owned end to end

• Experience leading a technical migration or rebuild - you've made the architecture calls and lived with them

• Strong Python: clean, tested, production code

• Strong SQL: you write complex queries, read execution plans, and think about how data is laid out

• Hands-on with a distributed processing engine - Spark, PySpark, or equivalent

• Cloud infrastructure: object storage, distributed compute, containers, CI/CD. GCP, AWS, or Azure

• Pipeline and orchestration experience - ETL/ELT, Airflow or similar, monitoring, data quality

• You can explain a technical trade-off to our Head of Product without a whiteboard

Nice to have

• Databricks specifically. Coming from BigQuery, Snowflake or Redshift is fine - we'll cover the gap

• Financial data: statements, market data, time series. If not, you should want to learn why accounting adjustments matter

• Unstructured data extraction - PDFs, filings, scraping

• AI/ML pipelines and serving model outputs into production

• dbt or similar transformation tooling

• Infrastructure-as-code