Data & Analytics Engineer

ABOUT HEALTHCARE INC•Saint Paul, MN

5d•Hybrid

About The Position

The Data and Analytics Engineer is a hybrid role responsible for delivering end-to-end analytics solutions—from data transformation and modeling to business intelligence and visualization. This role combines data engineering and reporting expertise to ensure data is accurate, well-structured, and delivered through intuitive dashboards and analytics tools that drive business decisions. Partnering closely with Intelligence, Data Engineering, and platform teams, this role builds and maintains scalable data models, curated datasets, and analytical views within the data platform, while also designing and developing dashboards, reports, and self-service analytics solutions. The Data and Analytics Engineer bridges technical data systems and business needs, owning the transformation and reporting layers while contributing to, but not owning, upstream pipeline and infrastructure design.

Requirements

Bachelor’s Degree in Computer Science, Information Systems, Data Analytics, or related experience
4–6 years of experience in analytics engineering, data engineering, business intelligence, or related roles
Strong experience working with large, complex datasets and cloud data platforms (e.g., Snowflake, Redshift, SQL Server)
Advanced principles of data modeling (dimensional and normalized) and semantic layer design
Understanding of ETL/ELT processes and modern data pipeline architectures
Data governance concepts including lineage, naming conventions, and data standards
Principles of data quality, validation, and testing methodologies
Understanding of business intelligence platforms and analytics ecosystems
Knowledge of applicable laws, codes, regulations, and data governance requirements (e.g., HIPAA)
Advanced proficiency in SQL for data transformation and analysis
Experience with data transformation tools (e.g., dbt, stored procedures, SQL scripts)
Ability to design, build, and optimize scalable data models
Ability to perform advanced data engineering tasks across ingestion, transformation, and delivery layers
Strong data quality practices with attention to detail and commitment to accuracy
Ability to document data models, field logic, and lineage in a clear, business-friendly format
Experience developing, consulting on, and optimizing dashboard and report packages (Tableau, Power BI, or similar)
Experience overseeing and supporting business intelligence and data analytical systems (e.g., semantic layer, BI platforms, access/permissions, content lifecycle)
Proficiency using standard, customized, and complex data analytics tools and techniques
Strong analytical, problem-solving, and communication skills; ability to translate complex data into actionable insights
Ability to interpret, apply, and explain applicable laws, codes, and regulations in practical scenarios

Nice To Haves

Prior experience working in a private equity-funded organization
Healthcare technology experience
Understanding of data governance principles, including naming conventions and lineage
Experience working with Git or version control in a collaborative environment
Knowledge of cloud ecosystems (e.g., AWS, Azure, GCP)
Master's degree in Information Technology or related field

Responsibilities

Business Intelligence & Analytics Delivery: Develop and maintain high-impact dashboards and reports using Tableau or similar tools. Translate business requirements into technical solutions by defining metrics, calculations, and aggregations. Support ad hoc analysis and ensure reporting outputs are aligned with trusted data models.
Translate business requirements into clear metrics, KPIs, and visualizations, ensuring alignment with organizational goals.
Partner with stakeholders to define reporting needs and deliver actionable insights and analysis.
Optimize report performance, usability, and adoption across business teams.
Data Modeling & Transformation: Design, build, and maintain scalable data models and transformation logic using SQL and modern data tooling (e.g., dbt). Develop standardized, reusable datasets and data marts that support consistent analytics across the organization. Optimize data structures for performance, scalability, and ease of use.
Data Pipeline & Quality Ownership: Partner with Data Engineering to support the design, development, and monitoring of data pipelines. Implement data validation rules, testing frameworks, and quality checks to ensure accuracy and reliability of data assets. Troubleshoot pipeline and data issues, performing root cause analysis and coordinating resolutions across systems.
Data Governance & Documentation: Document data models, business logic, and field-level definitions to ensure transparency and usability. Enforce data standards, naming conventions, and governance best practices. Support compliance with applicable laws, regulations, and data security requirements.
Platform Enablement & Self-Service: Enable self-service analytics by delivering curated, analytics-ready datasets and educating users on how to leverage them effectively. Support BI platform administration activities such as access management, content publishing, and performance optimization.
Cross-Team Collaboration: Partner with Intelligence, Product, Engineering, and Business stakeholders to understand data needs and deliver scalable solutions. Act as a bridge between upstream data engineering and downstream analytics, ensuring alignment and consistency across the data ecosystem.
Tableau Administration: Assist in managing Tableau environments, including user access, permissioning, content publishing, and basic server/site administration, as needed in collaboration with Sr Reporting & BI Developer.
Source Data Analysis & QA: Analyze source systems to understand data flows, structures, and anomalies that impact modeling. Ensures the accuracy of operational databases, reports, and related details through audits, queries, and operational reviews; works with teams to resolve discrepancies
Issue Resolution: Provide research, feedback to resolve management and customer questions and requirements; assists with receiving customer feedback and coordinating resources and responses as required. Troubleshoot and resolve data transformation, modeling, or pipeline issues in collaboration with broader data teams.
Process Improvement: Analyze system data to provide insights, draw conclusions, and provide recommendations for improving policies, data pipelines, modeling standards, and workflows/operations. Contribute to the efficiency and effectiveness of the department's service to its customers by offering suggestions and directing or participating as an active member of a work team. Train others in related policies and procedures, as needed.
Continuous Learning: Stay current on new tools, technologies, and best practices in data engineering, modeling, and analytics enablement.
Perform other duties as assigned.