Manager, Engineering, Enterprise Data Platform

As a Data Engineering Manager within the Enterprise Data Platform team at PitchBook, part of Morningstar, you will lead a team of skilled data engineers responsible for building and optimizing data pipelines, managing large-scale data models, and ensuring enterprise data asset quality and performance.

Key Responsibilities

• Lead, mentor, retain top talent, and develop a team of data engineers, fostering a culture of excellence, accountability, and continuous improvement.

• Define and drive the data engineering roadmap aligned with enterprise data strategy and business objectives.

• Collaborate with senior technology and business leaders to define data platform priorities, architecture, and long-term vision.

• Promote a culture of innovation, operational rigor, and customer empathy within the data organization.

• Oversee the design, development, and maintenance of high-quality data pipelines and ETL workflows across enterprise systems and PitchBook's platform.

• Ensure scalable, secure, and reliable data architectures using modern cloud technologies like Snowflake, Airflow, Kafka, and Docker.

• Lead AI learning initiatives and integrate AI and ML-driven solutions into data products to enhance automation and predictive insights.

• Champion data quality, governance, lineage, and compliance standards across the enterprise data ecosystem.

• Partner with engineering and infrastructure teams to optimize data storage, compute performance, and cost efficiency.

• Support career development through mentoring, skill-building, and performance feedback.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• 8+ years of experience in data engineering or related roles, including at least 2 to 3 years of leadership experience managing technical teams.

• Proven experience designing and maintaining large-scale data architectures, pipelines, and data models in cloud environments such as Snowflake, AWS, Azure, or GCP.

• Advanced proficiency in SQL and Python for data manipulation, transformation, and automation.

• Deep understanding of ETL or ELT frameworks, data orchestration tools like Airflow, and distributed messaging systems like Kafka.

• Strong knowledge of data governance, quality, and compliance frameworks.

• Excellent communication and stakeholder management skills with the ability to translate complex data concepts into business value.

• Demonstrated ability to build high-performing teams and lead through change.