Senior Data Engineer

The You Version Senior Data Engineer is primarily responsible for shaping, implementing, and maintaining data pipelines and systems that provide quality and reliable data. This role is critical in leading to increased engagement and growth through the Bible App globally. The Senior Data Engineer works across technical teams within the organization, including but not limited to Data Analytics, Software Engineering leaders, and Product teams.

This role is responsible for defining and executing data governance and architectural decisions and teaching/guiding others on the team. The Senior Data Engineer utilizes their skills to deliver technical outcomes that align with the direction of their team to further Life.

Church's mission and to reach people for Christ.

You Version was created by the local church in 2007 and remains a ministry of Life.

Church today. rch, our mission is to lead people to become fully devoted followers of Christ. Our team is committed to reaching people worldwide through innovative technology. And You Version is one of the ways we get to do that. Life.

Church is a multi-site Christian church meeting in the United States and globally rch Online.

We wholeheartedly believe a daily rhythm of seeking intimacy with God has the power to transform lives. That's why You Version creates biblically-based experiences that encourage and challenge people to seek God. We hope everyone in our community is on an active journey to become who God made them to be, abiding in Him, and drawing closer every day.

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., Big Query, 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…