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Tier-1 brand, common mid-level data engineer role, and broad cloud/Databricks skillset increase applicant competition.
Core data engineering skills are transferable, but healthcare domain knowledge provides moderate advantage.
Explicit 5–8 year requirement plus mandatory AWS, Databricks, Spark, and CI/CD skills make filters strict.
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Own design and implementation of scalable, cloud-based data engineering solutions using AWS services, Databricks, and Teradata Vantage to enable enterprise-wide advanced reporting and analytics.
Lead and manage deployment processes including CI/CD frameworks and DevOps tools (Github, Jenkins, Terraform, Liquibase) to automate data workflows, validation, and monitoring, minimizing manual interventions.
Deliver user-centric solutions in an Agile environment while ensuring technical excellence, peer code review, and alignment with enterprise architectural standards.
5 to 8 years of application program development experience or equivalent education and work experience.
Bachelor’s degree in Engineering, Computer Science, or related discipline.
Extensive hands-on experience with AWS services including Lambda, Step Functions, CloudTrail, CloudWatch, SNS, SQS, S3, VPC, EC2, RDS, and IAM.
Proficient in Python, Databricks, SQL, Unix, and experience with SPARK and real-time analytic frameworks.
Demonstrates strong ownership with experience leading complex, large-scale data engineering projects in Agile settings.
Skilled in automating and optimizing data workflows using cloud-native tools and CI/CD pipelines, indicating operational excellence and efficiency focus.
Experienced in collaboration with subject matter experts to translate business logic into scalable technical solutions, showcasing ability to bridge business and technology effectively.