• Support for Data Pipelines: Assist in the maintenance and development of pipelines that consolidate diverse sources (spreadsheets, analytics platforms, CRM systems, MMPs), helping to make data available as Data Marts for dashboards and stakeholders. • Data Quality Monitoring: Monitor existing data quality rules, helping to identify, report, and resolve inconsistencies to ensure the reliability of information. • Maintenance and Performance: Support the team in query optimization and day-to-day troubleshooting. • Documentation: Assist in creating and updating technical and business documentation for pipelines, models, and data rules. • Support: Help address requests from the marketing and data intelligence teams, learning to translate business requirements into data solutions.

• Design, develop, and support robust data infrastructure solutions with a focus on scalability, security, and efficiency. • Lead the implementation and maintenance of ecosystems such as Trino, AWS Glue, Redshift, Airflow, Kafka, and Spark on Kubernetes (K8s). • Collaborate with engineering, data science, and product teams to ensure seamless integration between systems. • Diagnose and resolve incidents related to performance, scalability, and reliability. • Apply and promote best practices in architecture, data governance, and monitoring.

• Collaborate with business users to identify, analyze, and document business requirements and key performance indicators (KPIs) • Design, develop, and maintain data integration and automation using Power BI, Azure Databricks, Power Automate, and SmartSheets • Collect and transform data from multiple sources into a structured format for analysis • Develop and maintain data models and data sets for efficient and accurate reporting • Create Power BI dataflows and shared semantic models to consolidate data model definitions • Ensure data accuracy, integrity, and consistency within the BI solutions • Provide training and support to Power BI developers to optimize data models in Power BI for their development needs • Collaborate with IT teams to ensure data security, integrity, and compliance with data governance policies • Stay up to date with the latest trends in business intelligence and analytics tools, techniques, and best practices

Role Description We're seeking a Data Engineer to design and implement data pipelines that power analytical capabilities. This hands-on role requires an understanding of data engineering best practices and the ability to translate business requirements into technical solutions. You will be part of a dedicated team creating datasets for analytic and data science workloads. You will work with other Data Engineers and Sr. Data Engineers on designing, implementing, and testing data pipelines. - Data Pipeline Development: Design and build ETL/ELT data pipelines to ingest, process, and transform datasets from multiple sources. - Performance Optimization: Implement best practices for performance tuning, partitioning, and clustering to optimize data queries. - Data Quality & Governance: Follow data quality standards, data governance frameworks, and security policies for data storage and access. - Data Modeling & Architecture: Develop and optimize data models and schemas to support analytics, reporting, and machine learning requirements. - Data Integration & Transformation: Collaborate with data scientists and analysts to design data solutions that integrate with BI tools and machine learning models. - Documentation & Knowledge Sharing: Create comprehensive documentation for data pipelines, workflows, and processes. Qualifications - 2+ years of applicable work experience - Proficiency in Python, specifically with ETL pipelines - Strong proficiency in SQL and experience in developing complex queries - Experience deploying data pipelines in a cloud environment (any of Azure, AWS, GCP) - Excellent communication and interpersonal skills, with the ability to collaborate effectively with data scientists and analysts. Requirements - Experience working with healthcare data, especially Epic. - Experience using GCP’s BigQuery - Knowledge of data governance best practices in a cloud environment. - Experience working with Machine Learning processes. Benefits - Comprehensive benefits package designed to support the physical, emotional, and financial well-being of colleagues and their families. - Medical, dental, and vision coverage - Paid time off - Retirement savings options - Wellness programs and other resources, based on eligibility.