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.

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

Staff Data Engineer

Key skills