| Detail | For this role |
|---|---|
| Department | Data and Analytics |
| Level | Manager |
| Reports to | Head of Data |
| Direct reports | Data Engineer, Big Data Engineer, ETL Developer |
| Experience | 8 to 12 years in data engineering with 2 years leading a team |
Data Engineering Manager job description template
Copy this job description, replace the text in square brackets and post it on your careers page or a job portal.
Job title: Data Engineering Manager
Department: Data and Analytics
Reports to: Head of Data
Location: [City], [office, branch or site]
About the role
A Data Engineering Manager leads the team that builds and runs the data pipelines and platform. They manage engineers, own pipeline reliability and data quality, and deliver the datasets analytics and applications depend on. The role sits below the head of data or architect. A good Data Engineering Manager ships reliable pipelines on time, keeps data fresh and correct, controls platform cost, sets good engineering practice, and builds data engineers into a dependable delivery team.
Key responsibilities
- Lead the data engineering team building and running the pipelines.
- Own pipeline reliability, data freshness and overall data quality.
- Plan and prioritise the data engineering backlog with stakeholders.
- Set engineering standards for pipelines, testing and code review.
- Deliver the datasets that analytics and applications depend on.
- Manage platform cost, performance and capacity with the team.
- Handle on call, incidents and root cause for data failures.
- Work with the architect on models, platform and standards.
- Hire, mentor and retain data engineers on the team.
Requirements
- Bachelor's or master's degree in computer science or related field
- Strong data engineering experience
- Some team leadership experience
- 8 to 12 years in data engineering with 2 years leading a team
KRAs and KPIs for a Data Engineering Manager
Key result areas for the appraisal form, each with a KPI you can measure every month or quarter.
| Key result area | How to measure it |
|---|---|
| Pipeline reliability | Data pipelines meeting freshness and uptime targets |
| Data quality | Data quality checks passing with few downstream defects |
| Delivery | Priority datasets delivered on the agreed schedule |
| Cost | Data platform cost held within the approved budget |
| Incidents | Data incidents resolved within the agreed response time |
| Team | Data engineers retained and growing in capability |
Skills and tools
Tools used day to day: Apache Spark, Airflow or similar orchestrator, SQL and Python, Snowflake or BigQuery, dbt and Git.
Reporting line and career path
Next roles: Head of Data, Data Architect, Director of Data Engineering
Interview questions for a Data Engineering Manager
- How do you keep data pipelines reliable and fresh?
- How do you build data quality checks into your pipelines?
- How do you handle a data incident that broke the morning reports?
- How do you control cloud data platform cost?
- How do you prioritise the data engineering backlog?
- How do you set engineering standards for your team?
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What does a Data Engineering Manager do?
A Data Engineering Manager leads the team that builds and runs data pipelines and the platform. They own pipeline reliability, data quality and delivery of the datasets analytics and applications need. They manage cost, incidents, standards and the engineers. Their job is reliable, fresh, correct data delivered on time.
What is the difference between a Data Engineering Manager and a Data Architect?
A Data Architect designs the platform, models and standards. A Data Engineering Manager leads the team that builds and operates pipelines within that design, owning delivery, reliability and the people. The architect sets the blueprint; the manager ships and runs it with the team.
What qualifications does a Data Engineering Manager need?
A degree in computer science or a related field, with strong data engineering experience and some team leadership. Employers value pipeline and ETL design, SQL and Python, cloud data platforms, data quality and cost control, and the ability to lead and retain engineers.