Design, build, and manage our data pipelines, ensuring all user event data is seamlessly integrated into our data warehouse.
Develop canonical datasets to track key product metrics, including user growth, engagement, and revenue.
Work collaboratively with various teams, including Infrastructure, Data Science, Product, Marketing, Finance, and Research, to understand their data needs and provide solutions.
Implement robust and fault-tolerant systems for data ingestion and processing.
Participate in data architecture and engineering decisions, bringing your strong experience and knowledge to bear.
Ensure the security, integrity, and compliance of data according to industry and company standards.
Requirements & Skills:
Have 3+ years of experience as a data engineer and 8+ years of any software engineering experience(including data engineering).
Proficiency in at least one programming language commonly used within Data Engineering, such as Python, Scala, or Java.
Experience with distributed processing technologies and frameworks, such as Hadoop, Flink, and distributed storage systems (e.g., HDFS, S3).
Expertise with any of the ETL schedulers such as Airflow, Dagster, Prefect, or similar frameworks.
Solid understanding of Spark and ability to write, debug, and optimize Spark code.
