Data Engineer

About the role

A leading entertainment company at the forefront of data-driven decision-making. This team focuses on optimizing retention strategies across streaming and television products, leveraging advanced data engineering techniques and cloud technologies to drive business impact.

Key Responsibilities

• Design, develop, and optimize ETL pipelines using Databricks and Snowflake.
• Collect, transform, and load (ETL) data into the warehouse and reporting environments.
• Optimize data performance and troubleshoot inefficiencies within Databricks.
• Work with large, unstructured datasets, managing complex joins and nested queries.
• Automate data dependencies and tasks, ensuring seamless data flow and pipeline reliability.
• Conduct daily monitoring of data flows to proactively resolve any errors or job failures.
• Build dashboards and visualizations in Tableau (not a requirement but a plus).
• Support retention analytics by enabling data-driven decision-making across customer segmentation models.
• Implement data partitioning and performance tuning techniques (e.g., Salt technique, Repartitioning) to optimize workloads.
• Work independently within an agile environment, collaborating with data analysts, business teams, and other engineering partners.

Required Qualifications

• 7+ years of experience in data engineering.
• Proficiency in Python, SQL, Spark, and Databricks.
• Experience working with Snowflake and cloud-based data platforms (AWS, Azure, or GCP).
• Strong understanding of ETL processes, data pipelines, and workflow automation.
• Experience working with churn, financial, subscription, and viewership data.
• Ability to track joins and manage nested, complex code structures in large datasets.
• Hands-on experience with data partitioning strategies for performance optimization.

Nice-to-Have Qualifications

• Experience developing Tableau dashboards.
• Familiarity with machine learning or predictive analytics in a data engineering context.
• Exposure to big data streaming technologies like Kafka or Spark Streaming.