Lead Data Engineer

The Lead Data Engineer role designs and develops complex, scalable data solutions using data integration tools and technologies. The individual utilizes modern data platforms and storage tools to create prototypes and data products utilizing multiple components and variables. Design, build, and test data pipelines and solutions. Additionally, the Lead Data Engineer integrates and tests data pipelines with Advance Analytics and AI platforms.

Principal Duties:

• Responsible for designing and implementing complex solutions for loading both structured and semi-structured data design into multiple target data systems.
• Design, develop, optimize, and maintain data pipelines and processes that adhere to data integration principles and business goals.
• Solve complex data problems by delivering insights that help our business to achieve their goals.
• Code, test, and document new or modified data systems to create robust and scalable applications for data analytics.
• Ensure that data pipelines are scalable, repeatable, and secure, and can serve multiple users within the company.
• Design and implement data ingestion techniques for real time and batch processes for structured and semi-structured data sources into Wawa's Raw, Refined, and Data Product layers of Wawa's enterprise data platform.
• Understand enterprise business areas and needs and translate into business requirements and propose end to end and simplified enterprise information architecture solutions.
• Design and implement data design methods, data structures, and modeling standards which work with multiple business intelligence tools.
• Work closely with Analytics team and implement their self-service and analytics requirements.
• Work with Data Science practitioners and developers to make sure that all data solutions are consistent.
• Collaborate with Analytics team to build solutions that enable business analytics.
• Develop quality scalable, tested, and reliable data services using industry best practices.
• Manage all activities centered on obtaining data and loading into a data lake environment.
• Assess the suitability and quality of candidate data sets for the data lake.
• Contribute to thought leadership in data solution technical design and implementation.
• Mentor to junior resources in technical best practices.
• Balance business requirements with technical feasibility and set expectations on new projects. Recommend and draft changes in development, maintenance and system standards.
• Design and build integration components and interfaces in collaboration with Architects and Infrastructure Engineers as necessary.
• Perform unit, component, integration testing of software components including the design, implementation, evaluation and execution of unit and assembly test scripts.
• Determine if the data received from the upstream systems are of good quality based on the rules and data quality validations defined and in case of any issues with the data quality analyze and come up with a preliminary summary of the root cause/issue.
• Assist the Analytics team by leveraging Wawa's Enterprise Data Platform ecosystem to design, and develop capabilities to deliver our solutions using Python, SQL, and Snowflake.
• Follow security standards for all data and tools that are being introduced in the team.
• Design and deliver certified data products (Bronze/Silver/Gold) with documented quality SLAs, lineage, and ownership.
• Partner with Data Governance on certification reviews, quality remediation, and policy adherence for production datasets.
• Build and maintain Snowflake-native transformation pipelines (SQL, stored procedures, tasks, streams, dynamic tables) with version control, testing, and CI/CD-gated deployment.
• Optimize warehouse cost and performance through query tuning, warehouse sizing, and consumption monitoring.
• Provide technical direction and design review for a workstream of 3-5 engineers; act as escalation point for production data issues.

Essential Functions:

• Handle multiple priorities simultaneously
• Work collaboratively with cross-functional teams
• Ability to build strong trusting relationships with business partners

• Passion for innovation and "can do" attitude to thrive in a fast-paced environment
• Ability to work in a fast-paced, team environment
• Excellent communication skills
• Basic project management skills required
• Work with the team to lead and maintain data strategy standards in all the data team is responsible for
• Ability to lead a team, * Bachelor's degree in Computer Science/Engineering preferred
• 8+ years database, data integration experience
• 5+ years experience with SQL, Scala/Python, Spark, and Modern Data Stack solutions
• 5+ years experience in designing and implementing data architecture (conceptual, logical, physical & dimensional models).
• Developing Enterprise Data & Analytics solutions on one or more of the following EDW platforms: Snowflake, Databricks, AWS, and BigQuery
• Experience implementing data solutions using open-source technologies
• Experience designing and implementing various data pipeline patterns and strategies
• Hands-on experience with dimensional modeling techniques and creation of logical and physical data models (entity relationship modeling, exposure to data warehouse design)
• Strong knowledge of data security principles
• Proven track record working with complex, interrelated systems and bringing that data together on enterprise platforms.
• Hands-on experience building Snowflake-native transformation pipelines using SQL, stored procedures, tasks, streams, and dynamic tables.
• Experience operating Snowflake at scale: warehouse sizing, cost monitoring, query optimization, and access control.
• Familiarity with data observability and quality tooling (e.g., Monte Carlo, Great Expectations, Snowflake data quality monitors) and applying it to certification SLAs.
• Demonstrated technical leadership: mentoring senior engineers, leading design reviews, and driving cross-team alignment on data standards.