Principal Data Engineer

Roles and Responsibilities

• Design and evolve canonical and medallion-layer data models (bronze/silver/gold) that enable scalable, governed data across the organization.
• Build and optimize ETL/ELT pipelines using Apache Airflow, Spark, Trino, and cloud-native tools.
• Develop high-performance data marts and semantic layers that serve analytics and data science needs.
• Architect streaming and analytical systems using Kafka and ClickHouse for real-time and batch insights.
• Define and enforce standards for data modeling, documentation, quality, and lineage across all domains.
• Partner with Analytics, AI/ML, and Infrastructure teams to translate business logic into reusable, trusted data assets.
• Mentor engineers, lead design reviews, and drive continuous improvement in scalability and data reliability.
• Design and evolve canonical and medallion-layer data models (bronze/silver/gold) that enable scalable, governed data across the organization.
• Build and optimize ETL/ELT pipelines using Apache Airflow, Spark, Trino, and cloud-native tools.
• Develop high-performance data marts and semantic layers that serve analytics and data science needs.
• Architect streaming and analytical systems using Kafka and ClickHouse for real-time and batch insights.
• Define and enforce standards for data modeling, documentation, quality, and lineage across all domains.
• Partner with Analytics, AI/ML, and Infrastructure teams to translate business logic into reusable, trusted data assets.
• Mentor engineers, lead design reviews, and drive continuous improvement in scalability and data reliability.

Principal Engineer Leadership expectation

• Lead multiple engineering teams or initiatives, providing technical direction, coaching, and feedback to foster growth and high performance.
• Operate as a cross-team engineer and advisor, proactively identifying opportunities, dependencies, and risks across products and platforms.
• Drive high-impact architectural and process improvements, reducing technical debt and enabling long-term scalability, resilience, and operational excellence.
• Represent Data Engineering with executive stakeholders, helping set technical strategy and translating business needs into forward-looking data capabilities.
• Establish engineering best practices and advocate for robust design, testing, quality, security, and monitoring standards across teams.
• Lead incident resolution for critical data platform issues, and cultivate a culture of knowledge sharing and blameless postmortems.
• Evaluate and recommend adoption of emerging technologies where they drive measurable business impact.
• Scale your impact through effective delegation, enabling teams to deliver autonomously while maintaining technical rigor and clarity.

Technical Qualification

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
• 10+ years of experience in Data Engineering, including 2+ years in a architectural-level technical role.
• Expertise in SQL, data modeling, and data mart design.
• Deep hands-on experience with Apache Airflow, dbt, Spark, Kafka, and ClickHouse.
• Proven experience designing medallion data architectures and scalable data lakehouse solutions.
• Proficiency in Python or Scala, and familiarity with AWS, GCP, or Azure data ecosystems.
• Strong understanding of data governance, lineage, and quality frameworks.
• Demonstrated ability to mentor engineers and influence architectural strategy across teams.
• Experience with real-time or streaming data (Kafka, Kinesis, or Pub/Sub).
• Knowledge of data observability and catalog tools (DataHub, Amundsen, Monte Carlo, Great Expectations, or Soda).
• Experience in eCommerce, retail analytics, or digital marketplaces.
• Exposure to governed data contracts and semantic layer frameworks.
• Proven track record of leading data architecture initiatives or cross-functional platform modernization.
• Contributions to open-source data tools or engagement in data community initiatives