Senior Data Engineer; Bankrate

Requirements

• 5+ years of data engineering experience designing and deploying large-scale data solutions
• Strong proficiency in SQL and solid skills in a modern language such as Python or Scala, with an emphasis on building reliable, performant data pipelines
• Hands‑on experience with Databricks, Spark, and AWS cloud services
• Strong understanding of data modeling, ELT/ETL design, and distributed data systems
• Familiarity with CI/CD and infrastructure‑as‑code tools (e.g., Git Hub Actions, Terraform, Airflow)
• Experience integrating third‑party data sources and external APIs into data platforms
• Experience supporting BI and analytics platforms such as Looker or similar
• Ability to communicate clearly with both technical and non‑technical stakeholders
• A collaborative mindset with a track record of working well across teams

• Bankrate is looking for a Senior Data Engineer to help build and strengthen the data platform that powers analytics and insights for millions of people making smarter financial decisions
• As a Senior Data Engineer, you’ll build and maintain the data systems that connect our products, analytics, and business teams
• You’ll take ownership of critical pipelines, contribute to platform improvements, and collaborate across disciplines to deliver reliable, high‑performing data solutions that drive measurable impact
• We’re building on Databricks and AWS using Terraform, so experience with these technologies is a major plus. You’ll work in a modern, cloud‑native data environment focused on accessibility, performance, and continuous improvement
• Build, maintain, and optimize data pipelines and infrastructure in Databricks and AWS (S3, EC2, Lambda, etc.)
• Audit and document existing pipelines to identify gaps, improve reliability, and strengthen data quality
• Integrate third‑party data sources and APIs into the platform, ensuring clean ingestion, transformation, and validation
• Partner closely with BI and analytics teams to translate business needs into scalable data models and solutions, bridging the gap between data engineering and the semantic layer
• Develop automated workflows for data ingestion, transformation, and validation
• Implement best practices in data architecture, security, observability, and governance
• Collaborate with data stakeholders on privacy‑safe data sharing solutions, including clean‑room environments and other secure data exchange methods
• Participate in code reviews and contribute to team engineering standards
• Support data migration efforts, moving data from internal systems to production‑grade environments
• Contribute to platform improvements that enhance performance, reliability, and cost efficiency