Manager of Data Engineering

Sheetz, Inc.•Pittsburgh, PA

51d•$99,067 - $165,114

About The Position

Provide oversight to the data engineering function and thought leadership for enterprise data solutions and delivery. Responsible for leading, mentoring, and coaching a team of data engineers. In collaboration with Business, Analytics and Data Science team, create data solutions that drive business value. Ensure Sheetz is building scalable and efficient solutions leveraging emerging technology (Databricks, AWS, etc..). Advocate thought leadership and help establish the product roadmap.

Requirements

Minimum 5 years' experience in data engineering, software development, or related fields required.
Minimum 3 years' experience with Modern Data Platforms required (Databricks, Snowflake, GCP)
Minimum 3 years' experience in people leadership of a team of data engineers required.
Bachelor's degree in Business, Information Systems or Computers Science is required.

Nice To Haves

Advanced certifications in cloud data engineering or specialized platforms (Databricks Certified Data Engineer Professional, Snowflake Advanced Architect, etc.) preferred

Responsibilities

Lead the enterprise Data Engineering team through designing, implement, testing and maintaining data pipelines that support business objectives.
Maximize the development of internal talent through regular performance coaching, performance reviews and presenting developmental opportunities
Collaborate with Product Teams, I&T professionals, and business partners to deliver scalable, secure, and efficient data solutions.
Provide thought leaderships around the data strategy and technical roadmap. Providing guidance on key technical decisions as well as implementation and adoption.
Manages the planning, prioritization and execution of data related projects aligning with business outcomes.
Communicate regularly with data analysts, data scientists, and other members of the Business Insights team.
Perform and monitor data governance, data quality and standards and understand downstream implication.
Define, measure, review, and implement the data engineering standards and best practices including data engineering processes, tools, and documentation.
Keep abreast of new features and functionality across the data ecosystem (Databricks, AWS, PowerBI, Tableau) and how to incorporate into Sheetz processes.