Senior Data Engineer

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The Senior Data Engineer is responsible for designing, developing, and maintaining enterprise data platforms, ETL pipelines, and analytics solutions that support regulatory reporting, business intelligence, and data-driven decision-making. This role plays a critical part in ensuring the efficient movement, transformation, and accessibility of data across multiple systems and cloud environments. Primary responsibilities include developing scalable data integration solutions, supporting analytics and reporting platforms, and collaborating with business and technical stakeholders to deliver high-quality data products.

Key Responsibilities

• Design, develop, and maintain scalable ETL and ELT pipelines using Python, PySpark, SQL, and cloud-based technologies.
• Build and support enterprise data integration solutions that move and transform data across multiple platforms and applications.
• Develop and optimize data pipelines utilizing technologies such as Apache Spark, Databricks, and cloud-native data services.
• Design, implement, and maintain data models and architectures that support reporting, analytics, and regulatory requirements.
• Create and support business intelligence solutions using Power BI and Tableau, with an emphasis on Power BI development.
• Collaborate with business stakeholders, analysts, and technical teams to translate requirements, user stories, and design specifications into technical solutions.
• Develop fault-tolerant, scalable, and maintainable data engineering solutions following software development best practices.
• Support cloud-based data platforms and services across Google Cloud Platform (Google Cloud Platform) and Microsoft Azure environments.
• Perform data validation, quality assurance, troubleshooting, and performance optimization activities.
• Work with structured and unstructured data from enterprise systems, databases, and third-party sources.
• Participate in all phases of the Software Development Life Cycle (SDLC), including requirements gathering, design, development, testing, deployment, and support.
• Maintain technical documentation, data lineage, and operational procedures for enterprise data solutions.

Minimum Education & Experience Requirements

Required

• Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or a related technical field.
• Minimum of 5 years of experience in data engineering, software engineering, or business intelligence development.
• At least 3 years of experience developing ETL and data integration pipelines using Python, PySpark, and SQL.
• At least 3 years of experience with Apache Spark, Databricks, or comparable big data technologies.
• At least 3 years of experience developing reports and dashboards using Power BI or Tableau.
• At least 3 years of experience working with enterprise databases such as Oracle, Teradata, DB2, or similar platforms.
• Experience working with cloud platforms, including Google Cloud Platform (Google Cloud Platform) and Microsoft Azure.
• Strong understanding of data architecture, data modeling, and data integration best practices.
• Experience participating in full Software Development Life Cycle (SDLC) processes.

Preferred

• Experience within healthcare, health insurance, population health, or regulatory reporting environments.
• Experience supporting HEDIS reporting and quality measurement initiatives.
• Experience with Informatica or similar enterprise ETL tools.
• Experience with cloud-native analytics and data warehouse technologies.

Special Requirements

• Strong proficiency in Python, PySpark, SQL, and Unix/Linux environments.
• Ability to support cloud-based data platforms and enterprise reporting systems.
• Ability to work effectively in a fast-paced, collaborative environment.
• Participation in after-hours support activities may be required as needed.

Knowledge, Skills, and Abilities

• Advanced knowledge of data engineering, ETL development, and enterprise data architecture.
• Strong programming skills in Python, PySpark, SQL, and scripting technologies.
• Experience with Apache Spark, Databricks, Dataproc, and cloud-based data services.
• Strong knowledge of relational databases, data warehousing, and data modeling concepts.
• Proficiency with Power BI and Tableau reporting platforms.
• Experience working with Google Cloud Platform and Azure cloud environments.
• Knowledge of software engineering principles, SDLC methodologies, and DevOps practices.
• Strong analytical and problem-solving skills.
• Excellent troubleshooting and performance optimization abilities.
• Strong written and verbal communication skills.
• Ability to manage multiple priorities and deliver high-quality solutions within established timelines.
• Strong attention to detail and commitment to data quality and accuracy.

Additional Desired Characteristics

• Experience supporting healthcare analytics, regulatory reporting, or quality measurement programs.
• Familiarity with HEDIS, healthcare data models, and clinical or claims data.
• Experience with enterprise data governance, metadata management, and data quality frameworks.
• Exposure to machine learning, predictive analytics, or advanced data science initiatives.
• Cloud certifications related to Google Cloud Platform, Azure, Databricks, or data engineering are highly desirable.

Work Environment

• Hybrid or remote work environment based on organizational requirements.
• Primarily office-based work with extensive use of computer systems and cloud-based technologies.
• Occasional travel may be required for team meetings, training, or project activities.
• Ability to support critical data processing activities during scheduled maintenance windows when necessary.