Data Engineering Manager Resume Example
The bar for Data Engineering Managers: team-driven infrastructure delivery. Resumes focusing on individual coding tasks get filtered out.
This resume is for data engineering managers who lead technical teams and own high-scale data infrastructure, but are not yet responsible for global data strategy or entire engineering departments.
- Evidence of shipping complex data infrastructure through a team of engineers
- Proven ability to balance technical debt with business-critical delivery
- Track record of growing engineering talent and maintaining high team retention
- Professional summary highlighting both people leadership and technical scale
- Experience section emphasizing team-wide metrics alongside technical initiatives
- Skills categorized by management competencies and technical toolsets
Destiny Washington
Summary
Experience
- Direct a team of 8 data engineers to build and maintain real-time ingestion pipelines processing 4.5B events daily for the core analytics platform.
- Spearheaded the migration of legacy batch processing to a streaming architecture, reducing data latency by 38% and saving $850,000 in annual infrastructure costs.
- Mentored 4 engineers through internal promotion cycles to Senior and Staff levels while maintaining a 92% team retention rate over 3 years.
- Established a self-service data platform using Terraform and Snowflake that decreased time-to-insight for product teams from 4 days to 6 hours.
- Architected a data observability framework to monitor 150+ production datasets, improving data uptime from 94% to 99.8% across the organization.
- Designed and deployed a Spark-based data deduplication engine that processed 2.5PB of historical logs, identifying $420,000 in storage redundancies.
- Refined the Airflow orchestration layer to support parallel execution of 500+ daily DAGs, increasing pipeline throughput by 45%.
- Owned the data modeling strategy for the merchant analytics dashboard, establishing schema standards adopted by 6 engineering squads to ensure cross-team consistency.
- Built the initial ETL pipeline for internal business intelligence using Python and AWS Redshift, supporting 120,000 active business accounts.
- Developed custom Airflow operators to automate data extraction from 12 third-party APIs, reducing manual data engineering toil by 30 hours per week.
- Optimized SQL query performance for executive reporting dashboards, cutting average load times from 12 seconds to 3.5 seconds.
Education
Skills
Python · SQL · Airflow · Spark · AWS · Data Modeling · Team Leadership · Hiring · Roadmap Planning · Stakeholder Management · Data Strategy · Performance Management · Snowflake · Terraform · Git
What makes this resume effective
- This resume meets the hiring bar for a data engineering manager by demonstrating high-scale infrastructure ownership, team development, and significant cost optimization.
- Notice how Destiny Washington quantifies her leadership impact at Amplitude by mentioning the specific number of engineers mentored through promotion cycles to Senior and Staff levels.
- The resume proves technical depth by referencing the migration from legacy batch processing to streaming architecture, resulting in a specific 850,000 dollar annual cost reduction.
How to write better bullet points
Managed a team of data engineers working on pipelines.
Directed a team of 8 data engineers to maintain real-time ingestion pipelines processing 4.5B events daily for the core analytics platform.
It specifies the team size, the scale of data handled, and the specific business platform supported.
Helped engineers get promoted and improved retention.
Mentored 4 engineers through internal promotion cycles to Senior and Staff levels while maintaining a 92% team retention rate over 3 years.
It provides concrete evidence of talent development and team stability using specific numbers.
Improved data quality across the organization.
Architected a data observability framework to monitor 150+ production datasets, improving data uptime from 94% to 99.8%.
It names a specific technical solution and quantifies the resulting improvement in reliability.
Data Engineering Manager resume writing tips
- Quantify team growth by listing specific promotions or retention rates to prove leadership success.
- Link technical migrations directly to business value, like infrastructure savings or reduced time-to-insight.
- Highlight your role in cross-functional strategy, such as setting schema standards across multiple engineering squads.
Common mistakes
- Focusing only on personal coding contributions instead of team delivery, which makes you look like a Senior Individual Contributor rather than a manager.
- Omitting people-management metrics like hiring speed, retention, or team member career growth.
- Failing to explain the purpose behind technical decisions, leaving out the business context that justifies the team's roadmap.
Frequently asked questions
Is this resume right for someone with only one year of management experience? Yes, if you can prove you’ve transitioned from ticket execution to owning team outcomes and growing engineers.
Yes, if you can prove you’ve transitioned from ticket execution to owning team outcomes and growing engineers.
Yes, if you have successfully transitioned from an individual contributor role and can show ownership of team outcomes. No, if you are still primarily focused on your own tickets and haven't yet been responsible for the performance or growth of other engineers.
What if my team is smaller than the eight engineers shown here? Focus on the complexity of your data pipelines and the business value delivered rather than specific headcount.
Focus on the complexity of your data pipelines and the business value delivered rather than specific headcount.
The specific headcount matters less than the scope of your responsibility and the impact of the team's work. Focus on the complexity of the data pipelines and the business value delivered rather than just the number of direct reports.
What if I don't have direct cost-saving metrics like the 850,000 dollars mentioned? Use any metric showing business impact, such as improved data freshness, reduced time-to-insight, or increased developer productivity.
Use any metric showing business impact, such as improved data freshness, reduced time-to-insight, or increased developer productivity.
Hiring managers look for any metric that shows business impact, such as improved data freshness or reduced time-to-insight. You can also highlight improvements in developer productivity or the reduction of manual engineering hours.
How much should I change before applying? Update specific technologies and metrics while keeping the structure that balances technical infrastructure with team-wide results.
Update specific technologies and metrics while keeping the structure that balances technical infrastructure with team-wide results.
Keep the structure of the bullet points that balance technical action with team results. Change the specific technologies and metrics to match your actual environment while maintaining the focus on leadership and scale.
What do hiring managers focus on at this level? Hiring managers look for the ability to manage both technical complexity and people development through architectural oversight and mentorship.
Hiring managers look for the ability to manage both technical complexity and people development through architectural oversight and mentorship.
Hiring managers look for the ability to manage both people and technical complexity. In Destiny Washington's resume, this is shown by balancing the management of 8 engineers with the architectural oversight of a 4.5B event-per-day pipeline.
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