Design, build, and manage data pipelines to integrate user event data into the data warehouse.
Develop canonical datasets to track key product metrics such as user growth, engagement, and revenue.
Collaborate with cross-functional teams (Infrastructure, Data Science, Product, Marketing, Finance, Research) to understand and address their data needs.
Implement robust and fault-tolerant systems for data ingestion and processing.
Participate in data architecture and engineering decisions.
Ensure data security, integrity, and compliance with industry and company standards.
Requirements :
3+ years of experience as a data engineer.
8 years of software engineering experience (including data engineering).
Proficiency in programming languages commonly used in data engineering, such as Python, pyspark, AWS, and Snowflake.
Experience with distributed processing technologies and frameworks (e. g., Hadoop, Flink) and distributed storage systems (e. g., HDFS, S3).
Expertise with ETL schedulers such as Airflow, Dagster, Prefect, or similar frameworks.
Solid understanding of Spark and ability to write, debug, and optimize Spark code.