Senior Data Engineer
Edwards Lifesciences
Senior Data Engineer
Job Description
Many structural heart patients suffer from heart failure with limited options. Our Implantable Heart Failure Management (IHFM) team is at the forefront of addressing these unmet patient needs through pioneering technology that enables early, targeted therapeutic intervention. Our innovative solutions are not just transforming patient care but also creating a unique and exciting environment for our team members. It’s our driving force to help patients live longer and healthier lives. Join us and be part of our inspiring journey. We’re seeking a Senior Data Engineer with strong enterprise experience delivering data product solutions on the Databricks Lakehouse Platform. The ideal candidate brings hands-on expertise building scalable, governed, production-grade data pipelines and analytics products using Databricks, Apache Spark, SQL, and CI/CD practices. This role focuses on transforming raw data into trusted, reusable data products that support analytics, reporting, and downstream applications across the enterprise.How you’ll make an impact:Apply hands-on expertise in Python, cloud technologies, Databricks, Apache Spark, SQL, and AI to build scalable and innovative data solutionsDesign, build, and maintain scalable, well-documented enterprise data products and ETL/ELT pipelines supporting batch and near-real-time processingMonitor, optimize, and tune data infrastructure and Spark/Databricks workloads to improve performance and cost efficiencyCollaborate cross-functionally with Data Science, Analytics, DevOps, Product, and Platform Engineering teams to deliver new features and data productsTroubleshoot and resolve data pipeline issues; ensure data quality through testing, monitoring, and CI/CD best practicesEnsure compliance with data privacy standards, including the handling of Patient Health Information (PHI), and adhere to enterprise security and governance policiesContribute to architecture discussions and implement best practices such as layered data modeling and Lakehouse design patternsPerform other incidental duties as assignedWhat you’ll need (Required):Bachelor’s degree in a related field, plus four years of experience in Data Engineering, DevOps, or Software Development -or- Master's degree plus three years -or- PhDStrong hands-on Databricks experienceAdvanced SQL skills for data transformation, analytics, and data modelingThis is an onsite role based in Irvine, CA. Relocation assistance is not provided, and candidates must reside within a 50-mile radius of Irvine to be considered.What else we look for (Preferred):Hands-on experience with cloud platforms (AWS preferred: EC2, S3, Lambda; Azure or GCP also acceptable)Experience with Databricks Workflows, Repos, Jobs, and cluster configurationProven experience building and deploying production-grade data workloadsProficiency in Spark (PySpark or Scala) with strong understanding of performance tuning and internalsExperience with Delta Lake (e.g., schema evolution, time travel, optimization)Understanding of Lakehouse architecture, medallion design, and enterprise data modeling standardsStrong foundation in databases, ETL processes, and data structuresExperience with data governance and security practices (RBAC, data masking, PII handling)Familiarity with data quality frameworks (e.g., Great Expectations, Deequ, or custom validation checks)Understanding of data contracts, SLAs, lineage, and data ownership modelsExperience with CI/CD pipelines (Azure DevOps, GitHub Actions, or GitLab CI) and version control using GitExperience delivering end-to-end data products (not just pipelines) for analytics, BI, and application use casesExperience working in regulated environments (e.g., healthcare, finance, or life sciences) preferredEligibility to work in the U.S. or EU, with the ability to travel as requiredAbility to comply with company policies, including Environmental Health & Safety and applicable workplace protocolsAligning our overall business objectives with performance, we offer competitive salaries, performance-based incentives, and a wide variety of benefits programs to address the diverse individual needs of our employees and their families.For California (CA), the base pay range for this position is $108,000 to $153,000 (highly experienced).The pay for the successful candidate will depend on various factors (e.g., qualifications, education, prior experience). Applications will be accepted while this position is posted on our Careers website. Edwards is an Equal Opportunity/Affirmative Action employer including protected Veterans and individuals with disabilities.
Locations
- USA - California – Irvine
Salary
108,000 - 153,000 USD / yearly
Skills Required
- Data Engineeringintermediate
- cloud platformsintermediate
- Databricks Workflowsintermediate
- and deploying production-grade data workloadsintermediate
- Sparkintermediate
- Delta Lakeintermediate
- data governanceintermediate
- data quality frameworksintermediate
- CI/CD pipelinesintermediate
Required Qualifications
- Bachelor’s degree in a related field, plus four years of experience in Data Engineering, DevOps, or Software Development -or- Master's degree plus three years -or- PhD (experience)
- Strong hands-on Databricks experience (experience)
- Advanced SQL skills for data transformation, analytics, and data modeling (experience)
- This is an onsite role based in Irvine, CA. Relocation assistance is not provided, and candidates must reside within a 50-mile radius of Irvine to be considered. (experience)
- Bachelor’s degree in a related field, plus four years of experience in Data Engineering, DevOps, or Software Development -or- Master's degree plus three years -or- PhD (experience)
- Strong hands-on Databricks experience (experience)
- Advanced SQL skills for data transformation, analytics, and data modeling (experience)
- This is an onsite role based in Irvine, CA. Relocation assistance is not provided, and candidates must reside within a 50-mile radius of Irvine to be considered. (experience)
Preferred Qualifications
- Hands-on experience with cloud platforms (AWS preferred: EC2, S3, Lambda; Azure or GCP also acceptable) (experience)
- Experience with Databricks Workflows, Repos, Jobs, and cluster configuration (experience)
- Proven experience building and deploying production-grade data workloads (experience)
- Proficiency in Spark (PySpark or Scala) with strong understanding of performance tuning and internals (experience)
- Experience with Delta Lake (e.g., schema evolution, time travel, optimization) (experience)
- Understanding of Lakehouse architecture, medallion design, and enterprise data modeling standards (experience)
- Strong foundation in databases, ETL processes, and data structures (experience)
- Experience with data governance and security practices (RBAC, data masking, PII handling) (experience)
- Familiarity with data quality frameworks (e.g., Great Expectations, Deequ, or custom validation checks) (experience)
- Understanding of data contracts, SLAs, lineage, and data ownership models (experience)
- Experience with CI/CD pipelines (Azure DevOps, GitHub Actions, or GitLab CI) and version control using Git (experience)
- Experience delivering end-to-end data products (not just pipelines) for analytics, BI, and application use cases (experience)
- Experience working in regulated environments (e.g., healthcare, finance, or life sciences) preferred (experience)
- Eligibility to work in the U.S. or EU, with the ability to travel as required (experience)
- Ability to comply with company policies, including Environmental Health & Safety and applicable workplace protocols (experience)
- Hands-on experience with cloud platforms (AWS preferred: EC2, S3, Lambda; Azure or GCP also acceptable) (experience)
- Experience with Databricks Workflows, Repos, Jobs, and cluster configuration (experience)
- Proven experience building and deploying production-grade data workloads (experience)
- Proficiency in Spark (PySpark or Scala) with strong understanding of performance tuning and internals (experience)
- Experience with Delta Lake (e.g., schema evolution, time travel, optimization) (experience)
- Understanding of Lakehouse architecture, medallion design, and enterprise data modeling standards (experience)
- Strong foundation in databases, ETL processes, and data structures (experience)
- Experience with data governance and security practices (RBAC, data masking, PII handling) (experience)
- Familiarity with data quality frameworks (e.g., Great Expectations, Deequ, or custom validation checks) (experience)
- Understanding of data contracts, SLAs, lineage, and data ownership models (experience)
- Experience with CI/CD pipelines (Azure DevOps, GitHub Actions, or GitLab CI) and version control using Git (experience)
- Experience delivering end-to-end data products (not just pipelines) for analytics, BI, and application use cases (experience)
- Experience working in regulated environments (e.g., healthcare, finance, or life sciences) preferred (experience)
- Eligibility to work in the U.S. or EU, with the ability to travel as required (experience)
- Ability to comply with company policies, including Environmental Health & Safety and applicable workplace protocols (experience)
- Aligning our overall business objectives with performance, we offer competitive salaries, performance-based incentives, and a wide variety of benefits programs to address the diverse individual needs of our employees and their families.For California (CA), the base pay range for this position is $108,000 to $153,000 (highly experienced).The pay for the successful candidate will depend on various factors (e.g., qualifications, education, prior experience). Applications will be accepted while this position is posted on our Careers website. (experience)
Responsibilities
- Apply hands-on expertise in Python, cloud technologies, Databricks, Apache Spark, SQL, and AI to build scalable and innovative data solutions
- Design, build, and maintain scalable, well-documented enterprise data products and ETL/ELT pipelines supporting batch and near-real-time processing
- Monitor, optimize, and tune data infrastructure and Spark/Databricks workloads to improve performance and cost efficiency
- Collaborate cross-functionally with Data Science, Analytics, DevOps, Product, and Platform Engineering teams to deliver new features and data products
- Troubleshoot and resolve data pipeline issues; ensure data quality through testing, monitoring, and CI/CD best practices
- Ensure compliance with data privacy standards, including the handling of Patient Health Information (PHI), and adhere to enterprise security and governance policies
- Contribute to architecture discussions and implement best practices such as layered data modeling and Lakehouse design patterns
- Perform other incidental duties as assigned
- Apply hands-on expertise in Python, cloud technologies, Databricks, Apache Spark, SQL, and AI to build scalable and innovative data solutions
- Design, build, and maintain scalable, well-documented enterprise data products and ETL/ELT pipelines supporting batch and near-real-time processing
- Monitor, optimize, and tune data infrastructure and Spark/Databricks workloads to improve performance and cost efficiency
- Collaborate cross-functionally with Data Science, Analytics, DevOps, Product, and Platform Engineering teams to deliver new features and data products
- Troubleshoot and resolve data pipeline issues; ensure data quality through testing, monitoring, and CI/CD best practices
- Ensure compliance with data privacy standards, including the handling of Patient Health Information (PHI), and adhere to enterprise security and governance policies
- Contribute to architecture discussions and implement best practices such as layered data modeling and Lakehouse design patterns
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