Director leading strategy, governance, and delivery of enterprise data platform at Phillips 66. Partnering with AI, Data Science, and business teams to enhance analytics and business systems.
About the role
- Senior Data Engineer creating analytical solutions and data pipelines for healthcare products at Press Ganey. Leading technology implementations and mentoring data engineering teams effectively.
Responsibilities
- Create data architecture, pipelines, and analytical solutions to meet software and data science requirements for various PG-MX Healthcare Products
- Identify, evaluate, select, and prove out new technologies and toolsets
- Create and execute Proofs of Concept and Proofs of Technology
- Lead and direct the work of others in data dependent projects
- Collaborate with software development, business teams, analysts, and data scientists to establish data storage, pipeline, and structure requirements
- Design, develop, and maintain ETL/ELT pipelines using Databricks (PySpark, Delta Lake, SQL)
- Implement Data Lakehouse architecture leveraging Databrick Unity Catalog, Delta Live Tables, and Workflows
- Build and optimize data ingestion frameworks for structured and unstructured data from diverse sources
- Identify and plan for data storage performance requirements
- Optimize Databricks clusters, jobs, and queries for performance and cost efficiency
- Identify impact of implementation on other applications and databases
- Lead and mentor data engineers on data projects
- Implement CI/CD for data pipelines using Git, Databricks Repos, and DevOps tools
- Ensure Data quality, reliability, security compliances across environments
- Build and evolve Trusted Record systems to manage entities across the enterprise
- Design, implement, and evolve solutions around person identity management.
- Develop and enforce data governance, lineage, and cataloging standards
- Identify areas of development and need, provide targeted training and exploration for team members.
Requirements
- Minimum of 5 years Data Engineering experience in an enterprise environment
- Bachelor’s degree in technology or like field required
- Hands on experience of Azure data technologies (Databricks, Data Factory, Stream Analytics, Data Lake Storage, Synapse), on-premises Microsoft tools (SQL DB and SSIS), and familiar with AWS data technologies
- Proficiency in Python, SQL and distributed data processing frameworks (Spark), and familiar with C#, PowerShell, and APIs
- Significant experience with analytical solutions in relational databases such as MS SQL Server, Oracle, and DB2 as well as experience with NoSQL databases and solutions such as data lakes, document-oriented databases, and graph databases
- Strong understanding of data modeling, schema design, and ETL best practices
- Experience with data lineage, cataloging, and metadata management in Databricks and Unity Catalog
- Skill in data modeling and experience with tools like ER/Studio or Erwin
- Familiarity with version control (Git) and DevOps/CI-CD practices
- Familiarity with SQL performance tuning and Spark optimization techniques
- Excellent problem-solving and communication skills.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Discretionary bonus or commission tied to achieved results
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