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Strong brand, metro location, mid-level generalist role increases applicant competition.
Data engineering skills are highly transferable across industries.
Multiple explicit years and mandatory tech requirements increase shortlisting rigidity.
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Design, develop, and maintain high-capacity, scalable data pipelines and API-based ETL/ELT processes for raw data collection from multiple sources.
Build and maintain data products including curated data sets for KPIs, semantic layers, data quality, observability, validation, lineage tracking, and performance monitoring.
Lead thought collaboration to scale data architecture and develop CI/CD processes and monitoring/alerting policies for data solutions.
5+ years hands-on experience in Python development.
4+ years experience with Databricks; 5+ years experience with advanced SQL query writing and optimization.
2+ years experience integrating with 3rd party APIs and working in AWS environment.
Bachelor's degree or equivalent in engineering or technical field (Computer Science, Information Systems, Statistics, Engineering, or similar).
Experienced in both backend data engineering and collaborating closely with business customers for requirements gathering and domain understanding.
Able to manage full data product lifecycle including advanced semantic layer development and rigorous data validation.
Familiar with software development best practices across the lifecycle including agile methodologies, code reviews, CI/CD, and deployment automation.