Data Engineering Manager

Role Summary

At Tovala, the Data Engineering Manager leads the Data Engineering team and owns the pipelines, platform, and data applications that every data consumer at the company depends on. They treat data engineering as an enablement function — partnering closely with Software and Data Analytics to understand what slows them down, then removing it so teams can ship faster and get to insights faster. This is a player-coach role: they manage and grow a team of data engineers while operating data pipelines and building and reviewing production code.

This role reports to the head of the Data team. They own technical data governance and monitoring across our stack (Snowflake, dbt, Airbyte, and Airflow), set the bar for pipeline best practices, and lead the team's adoption of AI-assisted workflows. As the team and product grow, they shape how the platform and its practices evolve. This role combines hands-on technical ownership with people leadership and cross-functional partnership.

How You Will Spend Your Time at Tovala

• Manage and grow a team of data and analytics engineers, and hire as the team grows
• Develop and build production pipelines and own problems end-to-end
• Own the team's technical roadmap, balancing platform investment and tech debt against delivery for the business.
• Empower our data analytics team to move faster and drive better outcomes by partnering closely with software engineering to design systems that ensure timely availability of high quality data
• Partner with Data Analytics to support source of truth datasets and metric definitions, so data means the same thing across every report and team
• Own data ingestion and pipeline reliability, from source onboarding through staged, analytics-ready data
• Own the architecture of the data platform and the design of shared datasets
• Lead the data applications function: set priorities, architecture, and quality standards for the models and tools the business depends on
• Own technical data governance: access, security, and privacy controls that meet legal requirements, plus lineage, quality checks, and data contracts
• Improve and enforce data engineering best practices, and review code with care
• Use AI-assisted development tools and workflows as a core part of how they work, and lead the team's adoption of these practices
• Drive alignment and decisions with stakeholders, including upstream design with software engineering

About You

• 5+ years in data engineering, including 1+ years as a people manager with direct reports. First-time managers with strong early results are welcome.
• Still writing production code today, with deep experience in dbt and an orchestrator such as Airflow on a cloud warehouse such as Snowflake.
• Experience designing data systems end to end, not just building within an existing design, and making sound architectural tradeoffs.
• Strong pipeline best practices: testing, CI/CD for data, modular modeling, code review and documentation.
• Hands-on experience with data monitoring and observability: alerting, freshness and quality checks, lineage and incident response.
• Experience implementing data access, security, and privacy controls, such as role-based access, PII masking, and retention.
• Experience preparing data for AI use, such as semantic layers or well-documented datasets that agents and LLM tools can query safely.
• Daily, practical use of AI tools in your own engineering work, and enthusiasm for helping a team adopt them.
• An enablement mindset: you measure success by how much faster other teams move, not by how much you control.
• Excellent communication, written and spoken, with technical and non-technical audiences.

Nice-to-haves

• Experience shaping a data platform roadmap over 12–18 months.
• Experience influencing application or event design upstream so data arrives clean and usable.
• Having run a formal governance program, or worked with tools for cataloging, access policies or data contracts.
• Experience with observability tools.
• Experience taking data products, models, or internal tools into production, and working with analysts or data scientists on them.