Senior Data Engineer supporting AI - enabled financial compliance initiative with data pipelines and ingestion processes. Collaborating with diverse teams in a mission - critical regulated environment.
About the role
- Data Engineer developing and maintaining data infrastructure at Highmark Health. Responsible for ensuring efficient and reliable flow of data across various systems.
Responsibilities
- Design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
- Develop and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
- Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
- Monitor work against the production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
- Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations
- Collaborate on the design and implementation of data security measures
- Perform data analysis and provide insights to support decision-making across various departments
- Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed
- Other duties as assigned or requested.
Requirements
- 3 years of experience in design and analysis of algorithms, data structures, and design patterns
- 3 years of experience in a data engineering, ETL development, or data management role
- 3 years of experience in SQL and experience with database technologies (e.g., MySQL, PostgreSQL, MongoDB)
- 3 years of experience in data warehousing concepts and experience with data warehouse solutions (e.g., Snowflake, Redshift, BigQuery)
- Proficiency in Python for data manipulation, scripting, API integrations, and developing robust data engineering solutions
- Strong SQL skills for complex data extraction, transformation, and loading (ETL/ELT) across various database systems
- Demonstrated experience with version control systems (Git/GitHub/GitLab) for collaborative development, code management, and deployment best practices
- Experience implementing and following SDLC best practices for data solutions
- Hands-on experience with Google BigQuery or comparable cloud-native data warehouse solutions
- Knowledge of data governance, data quality, and data lineage best practices
- Experience with dimensional modeling (Star Schema, Snowflake Schema) and other data modeling techniques
- Experience with Infrastructure as Code (IaC) tools like Terraform for managing cloud resources
- Experience building and managing data pipelines using workflow orchestration tools like Apache Airflow or Google Cloud Composer
- Proficiency with Google Cloud Platform (GCP) services relevant to data engineering, such as BigQuery, Cloud Storage, Cloud Functions, and Pub/Sub
- Familiarity with Data Lake/Lakehouse concepts and their application in modern data architectures
- Solid understanding and practical application of dbt (data build tool) for data transformation, testing, documentation, and version control of data models
- Experience with Starburst for data federation and analytics.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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