About
Responsibilities Contribute to maintaining, updating, and expanding existing data pipelines in Python / Spark while maintaining strict uptime SLAs Architect, design, and code shared libraries in Scala and Python that abstract complex business logic to allow consistent functionality across all data pipelines Tech stack includes Airflow, Spark, Databricks, Delta Lake, Snowflake, Scala, Python Collaborate with product managers, architects, and other engineers to drive the success of the Product Performance Data and key business stakeholders Contribute to developing and documenting both internal and external standards for pipeline configurations, naming conventions, partitioning strategies, and more Ensure high operational efficiency and quality of datasets to ensure our solutions meet SLAs and project reliability and accuracy to all our partners (Engineering, Data Science, Operations, and Analytics teams) Be an active participant and advocate of agile/scrum ceremonies to collaborate and improve processes for our team Engage with and understand our customers, forming relationships that allow us to understand and prioritize both innovative new offerings and incremental platform improvements Maintain detailed documentation of your work and changes to support data quality and data governance requirements
Basic Qualifications At least 5 years of data engineering experience developing large data pipelines Strong algorithmic problem-solving expertise Strong fundamental Python programming skills Basic understanding of AWS or other cloud provider resources (S3) Strong SQL skills and ability to create queries to analyze co
Languages
- English
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