• Lead and break down complex problems while keeping the broader data vision in focus. • Collaborate with engineers and engineering teams, providing mentorship and fostering a strong engineering culture. • Directly contribute to our data vision and engineering architecture. Produce and review technical artifacts that align with business goals. • Oversee engineering initiatives end-to-end, proactively managing risks, setting goals, and ensuring smooth execution and delivery. • Balance maintainability with tech debt and the development of new features.

• Designing, developing, and sustaining the comprehensive access controls and governance systems that improve the integrity and privacy of our data • Ensuring the reliability, scalability, and security of the data platform • Collaborating with various stakeholders and actively involve in the data infrastructure

• Design, build, and maintain robust data pipelines (ETL/ELT) • Work with Databricks for large-scale data processing using Spark and Delta Lake • Optimize data workflows, performance, and data storage solutions • Collaborate with cross-functional teams including data analysts, engineers, and stakeholders • Ensure data quality, reliability, and scalability across systems • Contribute to continuous improvement of data architecture and processes

• Monitor and manage overnight scraper and ingestion runs, triaging failures and applying fixes in real time to minimize data gaps before US market open • Verify data completeness and quality across all automated feeds, flagging anomalies and coordinating with the Houston team on persistent issues • Maintain run logs, error documentation, and escalation notes for seamless async handoffs • Build and maintain scrapers, parsers, and ingestion pipelines across a growing set of energy market data domains • Contribute to the design and build-out of our broader ETL infrastructure, including scheduling, orchestration, and error handling • Write transformation logic to clean, normalize, and load raw data into PostgreSQL staging and production tables • Optimize existing pipelines for performance, reliability, and cost efficiency • Help build monitoring dashboards and alerting for pipeline health and data freshness • Document data lineage, schema changes, and pipeline dependencies