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# Senior Data Engineer

- **Company:** Omaha Insights
- **Location:** Paris, France (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Cloud Computing, Continuous Integration, Extract Transform Load (ETL), Database Queries, Distributed Computing Environment, Python (Programming Language), Machine Learning, Operational Databases, Standard Sql, SQL Databases, Unstructured Data, Parquet, Data Logging, Snowflake, Apache Spark, Pyspark, Production Code, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** October 29, 2026
- **Apply:** https://www.welcometothejungle.com/en/companies/omaha/jobs/afd7caaa-ac69-4a92-8f3f-5c3fc896031a

## About the Role

* 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

## Description

* Own the migration from our current Python/Polars, GCS Parquet and Cloud SQL stack to Databricks and PySpark, from architecture through cutover
 * Set the technical direction: storage layout, orchestration, processing patterns, and the standards the team will work to
 * Keep the existing platform running and reliable throughout - clients don't get an outage while we rebuild

Data pipelines and quality

 * Build and run the pipelines that produce our financial datasets, optimising for performance, reliability and cost
 * Implement financial models and metrics in SQL and PySpark, working with our Head of Product on definitions
 * Own data quality as a system: validation, reconciliation, and alerting that catches breakage before clients do
 * Own monitoring, logging, and incident response for the data platform

AI and document processing

 * Build the pipelines behind our document extraction work - PDFs, filings, news - with our Head of AI
 * Prepare and serve data for AI models, and make their outputs available to downstream systems
 * Support the NL-to-SQL layer with the schema and semantics it depends on, * 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