• Support and maintain Census syncs between Snowflake and downstream tools. • Help design and document Census models and sync logic to ensure data reliability and consistency. • Monitor and troubleshoot data issues related to Census, Fivetran, and Segment pipelines. • Partner with Analytics to ensure upstream data supports activation and reporting needs. • Write and maintain SQL queries in Snowflake to support reporting and analysis. • Assist with ad hoc data requests and exploratory analysis for internal stakeholders. • Support the creation, maintenance, and updates of Tableau dashboards and published data sources. • Help validate data accuracy and investigate discrepancies surfaced through reporting. • Work with Analytics team members to refine data requirements and improve metric clarity. • Contribute SQL and documentation to shared repositories and knowledge bases. • Use tools like Notion, Git, and Jira to track work and communicate progress. • Support ongoing efforts to standardize definitions and improve data transparency.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Data Implementation Engineer is primarily responsible for the creation of processes that implement client data from hospital systems into company systems (data warehouse, data mapping tools, payment portals, collections platforms, etc.). This role also works closely with Operations, Workflow, Technical Support, Account Management, and other internal teams to ensure data integrity for clients post-implementation. Qualifications - BS in Mathematics, MIS, Computer Science, Statistics or comparable other technical background or combination of experience and/or education - 3-5 years of practical experience performing ETL between complex data sets and relational databases - Experience with MySQL, MSSQL, or other SQL and based platform - Experience with Talend Data Integration Requirements - Implement client data, implement new features on existing clients, and assist in data integrity for all clients - Implement client Revenue Cycle data into applicable applications based off project requirements including Benchmark and systems applications - Create and configure ETL processes from multiple data sources in complex formats - Log, track, and document solutions to complex data implementation tasks in Jira - Build custom data transformation processes using commercial ETL tools - Develop new logical data model schemas and tables for the creation and use of physical data model schemas and tables - Attend client project virtual meetings focused on gathering requirements and communicating project progress and timelines - Mentor other Team Members on data implementation solutions Benefits - Comprehensive paid training - Medical, dental, and vision insurance - HSA and FSA available - 401(k) with company match - Paid Wellness Time and Holidays - Employer paid life insurance and long-term disability - Internal growth opportunities

• Designs and builds scalable data extracts, integrations, transformations, and data models. • Ensures successful deployment and provisioning of data solutions across required environments. • Designs and implements data architectures and applications that enable speed, quality, and operational efficiency. • Interacts with cross-functional stakeholders to gather and define requirements and translate them into technical designs. • Develops deep familiarity with enterprise datasets, builds domain knowledge, and advances data quality. • Reviews requirements, identifies gaps, and drives resolution with stakeholders. • Identifies and recommends continuous improvement opportunities, ensuring integrations are automated, governed, and observable. • Serves as a key team member in designing and deploying a ground-up cloud data platform and pipeline. • Partners with data scientists to design, build, and maintain reproducible machine-learning pipelines. • Implements CI/CD for data and ML workflows. • Builds and maintains production-grade ML infrastructure such as feature stores, model registries, and data versioning. • Ensures ML models follow best-practice governance, including automated model performance monitoring and alerting. • Designs scalable data pipelines optimized for ML workloads. • Establishes MLOps standards, coding practices, and automation patterns that scale across teams.

• Build infrastructure for ingestion, transformation, and loading an exponentially increasing volume of data from a variety of sources using Spark, SQL, AWS, and Databricks • Building an organic entity resolution framework capable of correctly merging hundreds of billions of individual entities into a number of clean, consumable datasets. • Developing CI/CD pipelines and anomaly detection systems capable of continuously improving the quality of data we're pushing into production. • Dreaming up solutions to largely undefined data engineering and data science problems.