Full-Time Principal Data Engineer

As a Principal Data Engineer, lead the design, development, and support of data platforms for scalable and high-performance solutions, empowering data-driven decisions. Report to the Director of Data Platforms.

Collaborate with cross-functional teams to shape the future of data engineering, driving innovation and best practices.

Key Responsibilities:

1. Data Architecture & Strategy: Lead scalable data architecture using Databricks on AWS. Define long-term strategies, including technology roadmaps, data reference architecture, scalability plans, and integrating emerging technologies.
2. Data Warehouse Operations: Ensure the performance, reliability, and optimization of data warehouse infrastructure. Establish standards for query performance, storage efficiency, and data processing workflows.
3. ETL Development & Pipeline Management: Architect and oversee the development of complex ETL pipelines, processing and integrating data from multiple sources. Automate and streamline data ingestion, transformation, and delivery.
4. Data Quality & Governance: Implement robust data quality frameworks (accuracy, consistency, completeness). Define and enforce data governance practices for compliance and security.
5. Collaboration & Leadership: Partner with data scientists, product managers, and engineering teams. Mentor and guide a team of data engineers, fostering a culture of innovation and continuous learning.
6. Monitoring & Troubleshooting: Develop monitoring systems to identify and resolve performance issues, anomalies, and data discrepancies. Provide expert-level troubleshooting and support for complex challenges.
7. Documentation & Best Practices: Maintain detailed documentation of systems, processes, and configurations. Advocate for and implement coding standards, data governance policies, and security best practices.

Experience: 10+ years in data engineering, with at least 3 years in a leadership role. Expertise in data warehouse design, ETL pipelines, and data quality frameworks.

Technical Proficiency:Must Have: Advanced skills in Databricks, Cloud Platforms (AWS, Azure, or Google Cloud), SQL, Python, Spark, and ETL tools (Fivetran). Nice to Have: Experience with event tracking tools (Segment).

Problem-Solving Skills: Proven ability to solve complex data engineering challenges and derive insights from large datasets.

Mentoring & Collaboration: Experience in mentoring data engineers, and collaborating across diverse business functions.

Communication: Excellent written and verbal communication skills, articulating technical concepts to non-technical stakeholders.

Education: Bachelor's degree in Computer Science, Engineering, or a related field (Master's preferred).

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