About the role

Manager of Data Engineering developing and optimizing data platforms for enterprise analytics. Leading a team and collaborating with business stakeholders for high-quality data solutions.

Responsibilities

Lead, mentor, and grow a team of Data Engineers, including recruitment, onboarding, performance management, and continuous skill development.
Oversee the design, development, and optimization of data pipelines, architectures, and data sets for batch and streaming use cases.
Collaborate with business units and analysts to gather requirements and translate them into scalable data solutions.
Drive best practices in data modeling (conceptual, logical, and physical) to support analytics and reporting.
Establish and maintain metadata standards, data cataloging, and lineage to improve data discovery and governance.
Ensure timely delivery of projects by managing priorities, scope, and resources in an Agile framework.
Partner with vendors and managed service providers as required to support delivery and operations.
Define, track, and report on team KPIs such as data pipeline uptime, data freshness, and adoption metrics.
Contribute to capacity planning, cloud cost optimization, and vendor performance review.
Promote standardization and reusability of data models, pipelines, and integration approaches.
Serve as a subject matter expert, communicating technical solutions to both technical and non-technical audiences.
Maintain compliance with organizational standards for security, architecture, and development practices.
Encourage experimentation with automation, AI/ML integration, and modern data engineering practices.
Provide input into enterprise architecture and align data initiatives.

Bachelor’s degree in computer science, Engineering, or related technical discipline.
5+ years of experience in data engineering, including 2–3 years in a leadership or managerial role.
Proficiency in Python, SQL, and Spark (batch and streaming).
Experience developing and maintaining data lakes and cloud-based platforms (preferably Azure/Databricks).
Strong background in data architecture, including data modeling (conceptual, logical, dimensional) and relational databases.
Familiarity with data mesh or federated architectures for distributed data ownership.
Demonstrated ability to quantify business impact of data initiatives (e.g., cost savings, reduced downtime, increased adoption).
Knowledge of data governance, data quality, and master data management practices.
Proven ability to mentor and develop technical talent.
Hands-on experience with Agile/Scrum methodologies.
Excellent communication, problem-solving, and collaboration skills with a strong delivery-focused mindset.
Knowledge of orchestration tools (SSIS, ADF, DBT or equivalent).
Familiarity with DevOps/DataOps practices—CI/CD, version control, automated testing for data pipelines.
Exposure to streaming technologies like Kafka/Event Hubs beyond Spark Streaming.
Understanding of security and compliance frameworks (PII, GDPR, SOC2, HIPAA depending on industry).