Data Engineer - MoneyLion

Skills

About the Role

You will design, implement and optimize a Snowflake-centric architecture that leverages many components of the modern data stack. You'll act as a trusted subject matter expert on dimensional data modeling, performant SQL query writing, infrastructure optimization, process automation and governance. You'll get to use the latest AI tools, including Claude Code (Enterprise) and Snowflake's Cortex Code, to boost your work. You will design and implement an analytical environment using in-house and third-party tools, using Python and/or Java to automate data activities and enable efficient processing of data that is growing in both volume and complexity. You will build complex data pipelines and data models for analytical consumption, write scalable and performant SQL queries running over billions of rows of data, and design workflows to streamline governance across complex storage layers. You'll also mentor junior engineers and manage stakeholders from external departments as you help everyone at Gen Digital move toward an agentic AI-first analytics and development lifecycle.

Requirements

• Bachelor's degree in computer science, Data Engineering, or related technical field
• 1-4 years of hands-on data engineering experience in large-scale, high-volume environments
• Expert-level proficiency in SQL optimization for multi-petabyte datasets and advanced Python programming
• Experience with Snowflake architecture, optimization, and cost management at enterprise scale
• Demonstrated expertise building Kimball dimensional data warehouses and star schema architectures, or equivalent experience with thoughtful, non-generic projects
• Advanced experience with AWS cloud infrastructure and either DBT or SQLMesh for data transformations
• Familiarity with AWS is a big plus
• Experience in planning day to day tasks, knowing how and what to prioritise and overseeing their execution

Responsibilities

• Operate and optimize a modern data warehousing solution (Snowflake preferred) from performance and cost perspectives
• Apply expertise with Apache Kafka, real-time data processing, and event-driven architectures
• Apply experience with Apache Iceberg, data lakehouse architectures, and modern table formats
• Collaborate with data scientists, business analysts, product managers, software engineers and other data engineers to develop, implement and validate deployed data solutions
• Build and maintain data pipelines from internal databases and SaaS applications
• Implement solutions for data governance at scale, encompassing lineage, discoverability, and regulatory compliance
• Advocate best practices and uphold standards for code maintainability and performance
• Lead the team in honing ways of working, coaching and mentoring junior engineers, and managing senior stakeholders from external departments