Senior Data Engineer

What You'll Do

Deliver Trusted Data: Build and maintain reliable data pipelines that provide accurate, actionable data for analytics, experimentation, and decision-making.

Own Data Systems: Plan, implement, and operate ingestion, ETL/ELT, and integration workflows with a focus on quality, scalability, and resilience.

Partner Cross-Functionally: Collaborate with product, platform, analytics, and engineering teams to ensure relevant data is instrumented, collected, and usable.

Enable ML & LLM Use Cases: Prepare and curate datasets suitable for predictive modeling, experimentation, and LLM-driven applications.

Support Model Readiness: Design data pipelines and schemas that support training, evaluation, and inference workflows in partnership with ML-focused engineers.

Advance Data Governance: Contribute to data governance, stewardship, privacy, and security best practices.

Improve Observability: Build testing, monitoring, and alerting to ensure high data quality and early detection of issues.

Optimize for Scale: Performance tune pipelines, queries, and storage for efficiency and stewardship.

Document & Enable: Create clear documentation, diagrams, and data definitions to improve understanding across teams.

Mentor Others: Lead and support junior and mid-level data engineers through code reviews, pairing, and guidance.

Own Projects: Take responsibility for end-to-end delivery of data initiatives with minimal direction.

Grow Continuously: Stay current on data engineering, ML, and LLM-related tools, patterns, and best practices.

What It Takes to Thrive Here

Strong Ownership: Ability to independently lead complex data projects from concept to production.

Problem-Solving Mindset: Comfortable navigating ambiguity and solving complex technical challenges.

Collaboration Skills: Able to communicate clearly with both technical and non-technical partners.

Quality Focus: Strong instincts around testing, monitoring, and data correctness.

Learning Orientation: Curiosity and motivation to grow in ML- and LLM-adjacent data engineering practices.

Mission Alignment: Desire to use your skills to serve others and advance God’s Kingdom.

Technical Areas You Excel In

SQL: Strong proficiency writing complex queries and optimizing performance.

Programming: Experience with Python, Go, Java, or similar general-purpose languages.

Data Warehousing: Hands-on experience with warehouse design and modeling (e.g., BigQuery, Postgres, SQL Server, DBT).

Pipelines & Orchestration: Experience with Airflow, Pub/Sub, Fivetran, streaming platforms, or similar tools.

Cloud Platforms: Experience building data systems on GCP or comparable cloud environments.

APIs & Streaming: Experience integrating batch and real-time data sources.

ML Data Preparation: Experience preparing datasets for predictive, prescriptive, or classification models.

Feature Readiness: Understanding of feature engineering concepts and data requirements for ML workflows.

LLM Awareness: Familiarity with LLM concepts such as embeddings, prompt inputs/outputs, vector storage, or retrieval-augmented generation (RAG).

Pipeline Support: Ability to support data flows for model training, evaluation, and inference (without requiring deep model research).

Cross-Functional Partnership: Comfortable collaborating with ML engineers, data scientists, or platform teams on ML-enabled features.

What You Bring

Experience: 5+ years of data or software engineering experience building production-grade data systems.

Education: Bachelor’s degree in Computer Science, Data, or a related field (advanced certification a plus).

Technical Maturity: Proven ability to design, build, and operate reliable data pipelines.

Leadership Growth: Experience mentoring others and contributing to team-level technical direction.

Tooling Familiarity: Experience with tools such as GitLab, Jira, Amplitude, Backstage, or Notion is a plus.

Passion for Impact: Excitement about building data systems that support insight, learning, and spiritual growth.