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Tier-1 brand, popular data role, metro location, and broad platform requirements make competition high.
Core data engineering skills transfer across industries, but platform-specific tools raise moderate sensitivity.
Explicit 7–10 years requirement and mandatory platform experience (Databricks, Teradata, Kubernetes) increase strictness.
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Develop and maintain advanced data structures and pipelines to standardize, transform, and ensure data quality for insights generation and reporting.
Implement data ingestion frameworks for structured and unstructured data with quality checks and reporting, using platforms like Kubernetes, Teradata, and Databricks.
Mentor junior data engineers and contribute to internal tool development while providing strategic guidance in data engineering solutions.
Bachelor's Degree preferred; combinations of coursework and extensive related professional experience may be considered.
7-10 years of relevant work experience in data engineering or related fields.
Must be able to work nights, weekends, and variable schedules as necessary.
Experience with data platforms such as Kubernetes, Teradata, and Databricks required.
Experienced in designing and optimizing data flows with a strong focus on data quality and integrity across diverse storage and ingestion methods.
Able to provide technical mentorship and strategic data engineering guidance within a large, fast-paced technology environment.
Skilled in working collaboratively with management partners to enhance data sourcing, processing, and engineering tool frameworks for scalability and efficiency.