Data Engineering Manager

Machinify • Palo Alto, CA

93d

About The Position

As a Data Engineering Manager, you will lead a high-performing team responsible for transforming raw external and customer data into actionable, trusted datasets. Your team’s work powers product decisions, ML models, operational dashboards, and client integrations. You’ll combine hands-on technical expertise with people and project leadership, reviewing and designing production pipelines, mentoring engineers, and driving best practices. You will also be a key cross-functional partner, collaborating with product managers, Server teams, Platform teams, UI teams, SMEs, account managers, analytics teams, ML/DS teams, and customer success to ensure data is accurate, reliable, and impactful. This is a high-visibility role with both strategic and tactical impact — shaping data workflows, onboarding new customers, and scaling the team as the company grows.

Requirements

• Degree in Computer Science, Engineering, or a related field.
• 3+ years of combined technical leadership and engineering management experience, preferably in a startup, with a proven track record of managing data teams and delivering high-impact projects from concept to deployment.
• 10+ years of experience in data engineering, including building and maintaining production pipelines and distributed computing frameworks.
• Strong expertise in Python, Spark, SQL, and Airflow.
• Hands-on experience in pipeline architecture, code review, and mentoring junior engineers.
• Prior experience with customer data onboarding and standardizing non-canonical external data.
• Deep understanding of distributed data processing, pipeline orchestration, and performance tuning.
• Exceptional ability to manage priorities, communicate clearly, and work cross-functionally, with experience building and leading high-performing teams.
• Demonstrated experience leading small teams, including performance management and career development.
• Comfortable with ambiguity, taking initiative, thinking strategically, and executing methodically.
• Ability to drive change, inspire distributed teams, and solve complex problems with a data-driven mindset.
• Customer-oriented, ensuring work significantly advances product value and impact.

Nice To Haves

• Familiarity with healthcare data (837/835 claims, EHR, UB04).
• Experience with cloud platforms (AWS/GCP), databricks, streaming frameworks (Kafka/SQS), and containerized workflows (Docker/Kubernetes).
• Experience building internal DE tooling, frameworks, or SDKs to improve team productivity.

Responsibilities

• Lead, mentor, and grow a high-performing team of Data Engineers, fostering technical excellence, collaboration, and career growth.
• Own the design, review, and optimization of production pipelines, ensuring high performance, reliability, and maintainability.
• Drive customer data onboarding projects, standardizing external feeds into canonical models.
• Collaborate with senior leadership to define team priorities, project roadmaps, and data standards, translating objectives into actionable assignments for your team.
• Lead sprint planning and work with cross-functional stakeholders to prioritize initiatives that improve customer metrics and product impact.
• Partner closely with Product, ML, Analytics, Engineering, and Customer teams to translate business needs into effective data solutions.
• Ensure high data quality, observability, and automated validations across all pipelines.
• Contribute hands-on when necessary to architecture, code reviews, and pipeline design.
• Identify and implement tools, templates, and best practices that improve team productivity and reduce duplication.
• Build cross-functional relationships to advocate for data-driven decision-making and solve complex business problems.
• Hire, mentor, and develop team members, fostering a culture of innovation, collaboration, and continuous improvement.
• Communicate technical concepts and strategies effectively to both technical and non-technical stakeholders.
• Measure team impact through metrics and KPIs, ensuring alignment with company goals.

Benefits

• High Impact: Your team’s work powers key decisions across product, ML, operations, and customer-facing initiatives.
• Ownership & Growth: Influence the data platform and pipeline architecture while mentoring a growing team.
• Cross-Functional Exposure: Work with product, platform, engineering, ML, analytics, and customer teams to solve meaningful problems.
• Remote Flexibility: Fully remote with opportunities to collaborate across teams.
• Early Builder Advantage: Shape processes, standards, and practices as we scale.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: Bachelor's degree
• Number of Employees: 1,001-5,000 employees