Data engineering expertise: 8+ years of hands-on experience designing and optimizing complex data pipelines and ETL workflows, including error handling, monitoring, and performance tuning, using tools such as Snowflake, Databricks, Spark, Airflow, dbt, and Python. SQL and database mastery: Proficiency designing normalized schemas, optimizing complex queries, and applying advanced database features for high-performance analytical workloads.
Machine learning and statistical methods: Experience developing and optimizing ML solutions, designing and executing hypothesis testing strategies, and applying statistical techniques to validate analytical approaches. Web analytics and product telemetry: Familiarity with event tracking, clickstream data, sessionization, and user identity resolution, with demonstrated experience building analytical datasets from web and product usage data.
Data governance and quality: Experience implementing data standards, metadata management, lineage, ownership, and access controls, combined with the ability to design automated validation tests and anomaly detection to maintain data reliability. Communication and visualization: Ability to build complex, interactive visualizations and communicate findings clearly to both technical and non-technical stakeholders, with strong skills in independently managing stakeholder relationships.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.