Staff Data Engineer Resume Example
The bar for Staff Data Engineers: cross-team architectural leadership anchored to cost and scale. Tactical execution without roadmap influence gets filtered out.
This resume is for staff data engineers who architect cross-functional data platforms and set technical roadmaps, but aren't yet responsible for departmental headcount or engineering budgets.
- Evidence of cross-team technical leadership and roadmap influence
- Proven ability to architect systems that solve organization-wide scaling or cost challenges
- Deep technical mastery of distributed systems and modern data infrastructure
- Skills grouped by technical domain and leadership competencies
- Professional experience listed in reverse-chronological order
- Education section placed at the end of the document
Matthew Morgan
Summary
Experience
- Architected a unified data platform on AWS and Snowflake, consolidating 14 disparate data sources into a single source of truth for 1,200 internal users.
- Spearheaded a compute optimization initiative for Spark clusters, reducing monthly cloud infrastructure spend by $840K while maintaining processing latency.
- Defined the technical roadmap for the data platform organization, leading a group of 8 engineers to migrate legacy ETL pipelines to a modern Airflow-based orchestration system.
- Designed a real-time event streaming architecture using Kafka and Flink that supports 8M concurrent users with sub-second data freshness.
- Engineered a machine learning feature store using Python and Redis, decreasing model training time by 55% for the logistics optimization team.
- Optimized complex SQL transformations within the merchant analytics dashboard, improving query performance by 62% for 500K+ active partners.
- Mentored 4 junior data engineers on distributed systems design and advanced Python patterns, facilitating their transition into senior roles.
- Scaled the batch processing layer to handle a 4x increase in order volume during peak demand periods without increasing system downtime.
- Developed automated data quality frameworks using Great Expectations, identifying and resolving schema drift issues across 20+ microservices.
- Streamlined the CI/CD pipeline for data warehouse deployments, cutting deployment time from 45 minutes to 12 minutes.
- Built a customer usage analytics pipeline processing 200M events daily to drive product roadmap decisions for the core platform.
Education
Skills
Python · SQL · Airflow · Spark · AWS · Data Modeling · Kafka · Flink · Snowflake · Databricks · Data Architecture · Technical Leadership · Cost Optimization · Data Governance
What makes this resume effective
- BRIDGE: This resume meets the hiring bar for a staff data engineer by demonstrating cross-functional architectural ownership, significant cost optimization, and technical mentorship.
- At Canva, Matthew spearheaded a compute optimization initiative that slashed monthly spend by $840K, signaling the high-level business impact expected at this grade.
- The resume highlights org-wide influence by showing how he led a group of 8 engineers to migrate legacy systems to an Airflow-based orchestration platform.
How to write better bullet points
Built data pipelines for the marketing team using Spark and Airflow.
Architected a unified data platform on AWS and Snowflake, consolidating 14 disparate data sources into a single source of truth for 1,200 internal users.
It replaces a tactical task with an organization-wide architectural achievement that serves a massive internal user base.
Reduced cloud costs by optimizing Spark jobs.
Spearheaded a compute optimization initiative for Spark clusters, reducing monthly cloud infrastructure spend by $840K while maintaining processing latency.
It provides a massive, concrete financial metric and demonstrates leadership through a spearheaded initiative.
Mentored junior engineers on the team.
Mentored 4 junior data engineers on distributed systems design and advanced Python patterns, facilitating their transition into senior roles.
It quantifies the mentorship and defines the specific technical growth and career outcomes achieved for the mentees.
Staff Data Engineer resume writing tips
- Highlight initiatives where you influenced the technical direction of multiple teams or the entire organization.
- Lead with business-critical outcomes like massive cost savings or sub-second latency for millions of users.
- Emphasize your role in designing core infrastructure, such as unified data platforms or real-time streaming architectures.
Common mistakes
- Focusing on ticket completion rather than architectural strategy, which fails to show why you chose specific technologies for the business.
- Omitting the 'multiplier effect' by failing to mention how you mentored others or improved engineering standards across the organization.
- Listing too many tactical tools without context, suggesting a lack of the architectural breadth required for staff-level roles.
Frequently asked questions
Is this resume right for someone with only 6 years of experience? Yes if you can prove multi-team impact and architectural ownership; scope of influence matters more than total years of experience.
Yes if you can prove multi-team impact and architectural ownership; scope of influence matters more than total years of experience.
Yes, if you can demonstrate the multi-team impact and architectural ownership shown in Matthew's Canva experience. No, if your work is still confined to a single team's backlog or execution-only tasks.
What if I haven't worked at a high-growth tech company like Instacart? Yes, by highlighting how you managed scale, cost, and reliability within your specific industry context rather than relying on brand recognition.
Yes, by highlighting how you managed scale, cost, and reliability within your specific industry context rather than relying on brand recognition.
Focus on the complexity of the data problems you solved rather than the brand name. Highlight how you managed scale, reliability, or cost in your specific industry context to prove staff-level competency.
What if I don't have access to exact dollar amounts for cost savings? Use scale metrics like order volume increases, percentage-based efficiency gains, or the total number of internal users impacted by your work.
Use scale metrics like order volume increases, percentage-based efficiency gains, or the total number of internal users impacted by your work.
Use percentages or scale metrics, such as the 4x increase in order volume handled at Instacart. You can also describe the scope of users impacted, like the 1,200 internal users mentioned in this example.
How much of the technical stack should I change? Swap specific tools to match the target role while preserving the architectural context and high-level responsibilities like roadmap definition.
Swap specific tools to match the target role while preserving the architectural context and high-level responsibilities like roadmap definition.
You should swap specific tools like Kafka or Flink if your target role uses different technologies, but keep the architectural descriptions. Ensure the staff-level responsibilities like roadmap definition remain the focal point.
What do hiring managers focus on most at this level? They prioritize force multipliers who unlock productivity for dozens of engineers by solving systemic problems and defining the technical roadmap.
They prioritize force multipliers who unlock productivity for dozens of engineers by solving systemic problems and defining the technical roadmap.
They are looking for force multipliers who solve problems that unlock productivity for dozens of other engineers. In this resume, Matthew signals this by showing he defined the technical roadmap for the entire data platform organization.
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