Senior Data Infrastructure Engineer

What You’ll Do

• Own and maintain our data pipeline architectures (e.g., critical data ingestion services, ETL pipelines, database mirroring and warehousing), ensuring they are reliable, monitored, and meet SLAs.
• Manage and evolve our data modeling environments and provide a smooth, well-documented workflow for analysts and engineers.
• Operate and improve our orchestration systems (Dagster), ensuring jobs run reliably and are observable.
• Evaluate and rationalize data tooling from Databricks and notebooks (Marimo, Jupyter) to BI/analytics platforms (Redash and alternatives) and guide Voltus toward a sustainable, coherent data platform.
• Implement observability for data systems (logging, alerting, metrics) so issues are detected early and data quality is continuously monitored.
• Champion data governance and documentation, making datasets well-defined, trustworthy, and easy to navigate.
• Collaborate with analysts, data scientists, and platform engineers to ensure the infrastructure you build is intuitive, scalable, and solves real-world problems.
• Lay the groundwork for advanced applications by making Voltus’ data reliably accessible via well-documented interfaces, positioning us to adapt to future ML and AI use cases.

Who You Are

• Proven experience in a data engineering or infrastructure role, with responsibility for production-grade pipelines and data systems.
• Skilled in a programming language such as Python (Bonus Go)
• Deep experience with ETL/ELT pipelines, dbt, and integrating disparate data sources into warehouses/lakes.
• Familiarity with cloud data platforms (AWS, GCP) and modern data tooling. We are running on AWS.
• Experienced in workflow orchestration (Airflow, Dagster, or similar).
• Comfortable evaluating tradeoffs across notebook and analysis platforms (Jupyter, Marimo, Databricks) and recommending sustainable solutions.
• Knowledge of BI/analytics tools (Redash, Looker, Mode, Superset, etc.) and how to support or migrate to them.
• Strong understanding of data quality, governance, and observability.
• A clear communicator who can work across technical and non-technical teams to define requirements and deliver solutions.
• Comfortable taking ownership end-to-end of critical data infrastructure and serving as a point person for reliability and direction.
• Familiarity with observability/monitoring tools (e.g., Datadog, Prometheus).

Bonus Points

• Experience and Familiarity with Delta Lake, Databricks, DuckDB
• Exposure with LLM-based applications and toolchains (LangChain, LlamaIndex, lite-llm).
• Experience with vector databases (Pinecone, Qdrant, Chroma).