Candidates should possess a BSc/MSc in Computer Science or a related field, with 4+ years of industry experience, including at least 3 years in a data engineering or data-centric software engineering role. A proven track record of building and operating scalable, flexible, and always-on data pipelines is essential, along with fluency in data manipulation and SQL. Strong knowledge of Spark, Python (including libraries like pandas and polars), and Databricks (SQL Warehouses, Jobs Clusters, and Serverless) is required. Familiarity with DBT and Airflow is helpful, as is experience with cloud providers and cloud storage (AWS preferred), and efficiently working with datasets at TB scale. The ideal candidate is service and data quality oriented, passionate about shipping production quality code with good test coverage, and able to prioritize tasks and self-motivate without constant supervision.
The Data Engineer will be the primary owner of the data needs for a specific product vertical, managing everything from raw-data ingestion to end-user analysis. Responsibilities include building intuitive, easy-to-use, and high-quality datasets using Spark and DBT, tracking down and preventing data quality issues with monitors and alerts, and participating in an on-call rotation. The role involves understanding and documenting product vertical datasets, educating teams on their usage, and acting as a liaison between the product vertical and the core data infrastructure team to ensure business needs are met efficiently. Additionally, the engineer will support their vertical with data-related automation, potentially mentor junior engineers, and contribute to the strategic direction of data engineering across the company.
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