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Strong employer brand and metro location balanced by senior, specialized analytics engineering requirements.
Medium because core analytics-engineering skills transfer across industries despite streaming-specific product knowledge.
High due to explicit 9+ years requirement and mandatory technical stack and data-platform expertise.
Own end-to-end development of data pipelines, data models, and visualizations for the Commerce & Growth Analytics team.
Collaborate with cross-functional stakeholders to translate business questions into analytical solutions and semantic data layers supporting self-service.
Drive automation, data anomaly detection, and data quality assurance efforts to enhance scalability and reliability of analytics products.
Bachelor's degree or higher in quantitative fields such as Computer/Data Science, Engineering, Mathematics, or Statistics.
9+ years of experience in business intelligence or data engineering.
Proficient in SQL with deep knowledge of data warehousing concepts (star schemas, SCD, ELT/ETL) and experience with big data technologies like Spark, Kafka, Hive.
Experience building reports and dashboards using BI tools such as Looker or Tableau; strong programming skills in Python, Java, or Go.
Experienced in managing complex data engineering projects from design through production with minimal supervision in fast-paced environments.
Strong focus on data quality, scalable automation, and building frameworks or tools that enhance team productivity and cross-team usability.
Familiarity with cloud data platforms (e.g., Databricks), OLAP databases (Snowflake, Redshift), and semantic layer frameworks (dbt) is a plus but not mandatory.