Data Engineering Manager

Data Platform Manager / Hands-On Data Engineering Manager Direct reports: Senior Data Engineer and Data Engineer Position Summary We are seeking a hands-on Data Platform Manager to lead the development and operation of the data platform supporting our fleet and telematics products. This role combines people leadership with substantial individual technical contribution: you will guide and mentor a small data engineering team while personally designing, building, and troubleshooting production pipelines. You will work closely with Data Science, product, and engineering teams to move data from ingestion through transformation to the datasets and views that support analytics and customer-facing applications. You will help set platform direction, balancing delivery needs with data quality, reliability, security, performance, and cost. Responsibilities Hands-On Engineering and Platform Ownership
• Design, build, and maintain production data pipelines and orchestration workflows from ingestion through transformation and serving
• Write and optimize SQL, Python, and PySpark for large-scale data processing
• Develop and operate workflows using Apache Airflow or a comparable orchestration platform
• Build and maintain data solutions using Snowflake and AWS services, including S3 and EMR/Spark
• Develop and improve streaming or event-driven ingestion patterns, including Kafka where appropriate
• Set up data quality checks, pipeline monitoring, alerting, and operational dashboards
• Investigate production issues, lead root-cause analysis, and implement durable corrective actions
• Improve warehouse performance and control platform costs through query optimization, resource sizing, and scheduling strategies
• Contribute directly to code reviews, technical design, testing, and deployment Team Leadership
• Manage and mentor the Senior Data Engineer and Data Engineer, providing technical guidance, feedback, and development support
• Set clear priorities and delivery expectations, balancing project work with platform health and operational responsibilities
• Support team members in design reviews, troubleshooting, and delivery planning while encouraging ownership and sound engineering practices
• Coordinate work across data engineering, Data Science, product, and software engineering teams
• Communicate progress, risks, dependencies, and technical tradeoffs to stakeholders Platform Direction and Collaboration
• Establish practical standards for data architecture, pipeline development, testing, documentation, and production readiness
• Partner with Data Science to support feature engineering and data pipelines for machine learning and analytics workloads
• Collaborate with product and engineering stakeholders to translate business needs into reliable and scalable data solutions
• Document platform architecture and key pipeline designs to support maintenance and onboarding
• Identify opportunities to simplify, modernize, or automate the data platform as product needs and data volumes grow Required Qualifications
• 5+ years of experience in data engineering, including hands-on ownership of production data pipelines
• Experience leading, mentoring, or managing data engineers
• Strong SQL skills, including query analysis and optimization
• Proficiency in Python and PySpark for distributed data processing
• Hands-on experience with a cloud data warehouse such as Snowflake
• Experience with workflow orchestration, such as Apache Airflow
• Experience with AWS data infrastructure, including cloud storage and distributed processing such as S3 and EMR/Spark
• Experience building and operating data pipelines in production, including monitoring, data quality, and incident response
• Understanding of data warehouse design, data modeling, and ETL/ELT patterns
• Strong communication skills and the ability to explain technical decisions to technical and non-technical stakeholders Preferred Qualifications
• Experience with Kafka or other streaming and change-data-capture (CDC) technologies
• Experience with relational databases such as PostgreSQL
• Familiarity with data quality and observability frameworks
• Exposure to ML and analytics pipeline support, including feature engineering or model-serving data preparation
• Familiarity with monitoring and logging tools such as Dynatrace, Prometheus, Grafana, or the ELK stack
• Experience with CI/CD, infrastructure automation, and DevOps practices
• Familiarity with Docker and container orchestration platforms such as Kubernetes or OpenShift
• Experience with collaboration and delivery tools such as Jira, Bamboo, Bitbucket, or Confluence
• Relevant certifications, such as SnowPro or AWS data certifications What Success Looks Like
• The manager remains an active technical contributor while enabling the engineers reporting to them to deliver effectively
• Production pipelines are reliable, observable, well-documented, and supported by meaningful data quality checks
• Data is delivered consistently to customer-facing products, analytics, and Data Science workloads
• Platform performance and cost are actively managed as data volume and usage grow
• Incidents lead to clear root causes and completed improvements
• The team has clear priorities, strong technical standards, and productive cross-functional partnerships