Data Engineering Manager

Cambria•Belle Plaine, MN

17h•$138,000 - $179,000•Hybrid

About The Position

Cambria is looking for a hands-on Data Engineering Manager to lead the engineering function and empower decision-making with reliable, scalable, accessible data. In this role, you will serve as both a technical contributor and a people leader: setting the engineering strategy for the pod, hiring and developing the team that builds it, and partnering closely with data scientists, BI engineers, and the Analytics Product Owner to turn complex business questions into data products that ship. You move easily between architecture conversations and code review, executive readouts and sprint plans. You set the technical bar for quality, scalability, and security. You take smart risks, you protect your team’s focus, and you make sure they have what they need to do the best work of their careers.

Requirements

Deep proficiency in SQL and Python.
Strong working knowledge of cloud data architecture, distributed systems, and modern ELT/ETL patterns.
Production experience with Snowflake (Medallion architecture a plus), dbt, and cloud platforms (AWS preferred).
Familiarity with ingestion tools like FiveTran or Qlik and BI platforms like Sigma, DOMO, or Tableau.
Hands-on experience with distributed data processing at scale — Spark, Hive, Kafka, modern table formats like Apache Iceberg, file formats like Parquet, and dependency-driven job schedulers.
Strong instincts for data modeling, schema design, and task estimation.
Comfortable with API specs, identifying the right calls, extracting the data, and shaping it into something analysts can actually use.
A track record of building, growing, and mentoring data engineering teams. You manage technical debt, set KPIs, run performance reviews, and develop the next layer of leaders below you.
Comfortable translating between business strategy and engineering execution. You navigate complex cross-functional situations with diplomacy. You speak clearly to executives without dumbing it down.
Calm under pressure.
Excellent attention to detail.
Able to deliver high-quality work in a dynamic environment, against tight deadlines and competing requests.
A passion for building inclusive engineering culture and cultivating psychological safety.
An eagerness to learn.
A sense of humor.
Bachelor’s degree in Computer Science, Engineering, or a related quantitative field. Master’s preferred. Relevant industry experience is valued over formal credentials.
Significant experience in data engineering, including a proven track record managing data engineering teams. We value depth and breadth of experience over years on a resume.
Systems: Snowflake, dbt, AWS, ELT/ETL tooling (Qlik, FiveTran, or similar), and modern BI platforms (Sigma, DOMO, Tableau, or similar).
Manages a team of data engineers; partners cross-functionally with Data Science, BI, Market Research, Analytics Product Management, and senior business stakeholders.
Able to lift up to 20 pounds occasionally / sparingly (office supplies, packages, samples). May need to bend, reach, walk, or stoop occasionally. Prolonged periods sitting at a desk and working on a computer.

Nice To Haves

Medallion architecture a plus
Master’s preferred

Responsibilities

Lead the team. Actively recruit, hire, mentor, and grow data engineers at every level. Set clear goals. Foster a culture of ownership, curiosity, and inclusion. Create the conditions for people to do the best work of their careers.
Drive engineering strategy. Design and oversee the data pipelines, warehouse models, and ELT processes that fuel GTM analytics. Collaborate with data architects on trade-offs — performance, cost, complexity, maintainability.
Shape the stack. Partner with Analytics and Architecture leaders on critical decisions about Snowflake, AWS, dbt, ingestion tooling, and BI platforms. Set the technical bar for quality, scalability, observability, and security.
Translate business needs into engineering plans. Work closely with Data Science, BI, Market Research, and the Analytics Product Owner to turn complex business questions into pipelines, reports, and data products that ship.
Establish the standards. Define and incorporate best practices for data quality, security, privacy, and governance in partnership with architecture. Manage technical debt before it manages you.
Run the cadence. Partner with the APM on roadmap and sprint planning. Deliver high-priority data products on time, with quality, in a fast-moving environment.
Bridge engineering and business. Speak both languages fluently. Communicate progress, risks, and strategic direction to senior leadership with clarity and confidence. Cover ad-hoc analytical tasks and automate them away over time.