Senior Data Engineer: Data Warehouse & Reporting

US Software Solutions is a software development company focused on providing digital technologies to the Utilities, Telecommunications, Energy Efficiency, Renewable Energy, and Gas/Electricity sectors. Thanks to a team consisting exclusively of experts in their field, we are ready to supervise any project, regardless of its complexity and scale.
A Senior Data Engineer responsible for building and managing the company’s data warehouse and reporting infrastructure. The role ensures that data from different systems and APIs is reliably collected, transformed, stored in PostgreSQL, and prepared for accurate and efficient Power BI reporting. In short: they own the data pipeline from source systems to the final reporting layer, while ensuring data quality, performance, reliability, and maintainability.

Work schedule:

Monday - Friday from 14:00 till 22:00

• Own ingestion pipelines from source systems into the warehouse: extraction, staging, transformation, publication.
• Design the warehouse model — layering, grain, incremental load strategy, historization, change handling.
• Write and optimize substantial SQL; transformation logic and reporting-layer performance both live there.
• Build and maintain Airflow DAGs with sound scheduling, dependencies, retries, idempotency, and backfill behavior.
• Integrate new sources, internal and third-party.
• Guarantee data quality — reconciliation against sources, validation, and alerting when a load is wrong, not just when it fails.
• Work with the BI team on the semantic layer and report performance; design upstream models that make reporting straightforward.
• Improve pipeline maintainability, testing, and observability.
• Set data engineering standards and mentor others.

• 6+ years in data engineering, or backend engineering with a heavy data focus.
• Strong Python, and production Airflow experience — DAG design, scheduling semantics, idempotent tasks, backfills, debugging live failures.
• Expert SQL on PostgreSQL: complex analytical queries, execution plans, and knowing why something is slow.
• Solid warehouse modeling — dimensional design, slowly changing dimensions, grain, and incremental patterns.
• Experience building incremental ingestion from REST APIs: pagination, rate limits, retries, late-arriving data.
• Practical data quality discipline — reconciliation, validation, pipelines that fail loudly rather than quietly.
• Working knowledge of Power BI and the Microsoft BI stack; enough to model for it and to debug a slow or incorrect report.
• Containers and CI/CD; able to own your pipelines in production.
• Experience shipping production code with AI coding tools, and the critical eye that comes with it.