Senior Backend Engineer - Data Pipelines (US Market)

About the role

Deda, a technology group with operations in the United States, is hiring a Senior Backend Engineer for its US market. As Senior Data Engineer, you own the pipelines that turn core banking data from US credit unions and master data from industrial distributors into analytics, lending and marketing decisions. The data is regulated (GLBA, FCRA), so lineage, access control and reproducibility are design constraints.

Key responsibilities

• Ingestion and integration: build and harden the file and fileset ingestion pipeline, including schema detection, late or missing files, idempotent loads and failure reports, and design future direct pulls from client APIs or databases
• Data modeling: own and evolve the shared credit-union data model and per-product marts, including schemas, migrations, constraints and indexes
• Orchestration and transformation: define pipelines as Dagster assets with partitions, schedules, sensors, backfills and asset checks, using SQL in PostgreSQL and Polars or Pandas where needed
• Data quality and observability: build freshness, volume, referential integrity and distribution drift checks, and own the runbooks when they fire
• Enablement: give data scientists, analysts and LLM engineers stable, documented datasets, turn notebooks into assets, and set naming, testing, review and CI conventions on GitHub Actions
• Regulated data: apply least-privilege access, PII handling, lineage and audit trails so the origin of data is traceable

Required profile

• Experience as a Data Engineer or Backend Engineer on data-heavy systems, owning production pipelines end to end
• Fluency in SQL and production experience with a relational database (PostgreSQL preferred)
• Production experience with a modern orchestrator (Dagster preferred) and the concepts behind it
• Solid Python with a dataframe library (Pandas and/or Polars preferred), including the judgment to push work to the database
• Experience loading messy data from CSV, fixed-width, Excel, Parquet, JSON, SFTP or object storage, or client APIs
• Ability to design a normalized core model and a denormalized mart, and to treat pipelines as software with Git, code review, tests, CI/CD and Docker

Nice to haves

• Dagster in depth, Polars and the Arrow ecosystem, SQL Server, GCP or another cloud, Kubernetes with GitHub Actions
• Banking or regulated data (GLBA, GDPR), master data and entity resolution, feature tables for ML/LLM, analytics engineering (dbt, Metabase, Superset, Looker)