Lead Data Engineer

MUST HAVE:

• 5+ years of experience in data engineering within fast-paced, cloud-native environments
• Deep expertise in Python, SQL, Docker, and AWS (S3, Glue, Kinesis, Athena/Presto)
• Experience building and managing scalable ETL pipelines and data lake infrastructure
• Familiarity with distributed systems, Spark, and data quality best practices
• Strong cross-functional collaboration skills to support BI, analytics, and engineering teams

EXPERIENCE: 5+ years of data engineering experience in an AWS-based environment where data powers decision-making across product, marketing, and operations.

YOUR ROLE

Lead scalable data engineering efforts that empower cross-functional teams with reliable, timely, and actionable data, ensuring Launch Potato’s analytics and business intelligence infrastructure fuels strategic growth.

OUTCOMES

• Build and optimize scalable, efficient ETL and data lake processes that proactively catch issues before they impact the business
• Own the ingestion, modeling, and transformation of structured and unstructured data to support reporting and analysis across all business units
• Partner closely with BI and Analytics to deliver clean, query-ready datasets that improve user acquisition, engagement, and revenue growth
• Maintain and enhance database monitoring, anomaly detection, and quality assurance workflows
• Serve as the internal point of contact for reporting infrastructure—delivering ad hoc data analyses and driving consistent data integrity
• Drive adoption and advancement of Looker dashboards by ensuring robust and scalable backend data support
• Contribute to the future of Launch Potato’s data team by mentoring peers and shaping a high-performance, quality-first engineering culture

COMPETENCIES

• Data Engineering Mastery: Demonstrates technical excellence in building data pipelines, troubleshooting distributed systems, and scaling infrastructure using AWS and open-source tools
• Cross-Functional Collaboration: Communicates clearly across technical and non-technical teams, translating business needs into robust data solutions
• Proactive Ownership: Operates with a strong sense of accountability; identifies and solves data issues independently and efficiently
• Quality-Driven Execution: Holds a high bar for data accuracy, auditability, and documentation throughout all systems and workflows
• Strategic Thinking: Anticipates how data infrastructure impacts wider company OKRs and proactively suggests improvements and innovations
• Growth Mindset: Seeks out opportunities to elevate team capabilities, mentor others, and stay ahead of evolving best practices in data engineering