Data Analytics Engineer

Data Analytics Engineer Location: Alpharetta, GA, US Industry: Hospital and Healthcare Work Setting: Hybrid Are you passionate about building trusted data platforms, improving data quality, and delivering scalable analytics solutions that empower healthcare and business stakeholders with reliable, actionable insights?

We are seeking a Data Analytics Engineer to design, build, and maintain modern data solutions that support analytics, operational reporting, and enterprise decision-making. This role will focus on developing governed, reusable data assets, strengthening data quality, reducing technical debt, and implementing scalable data engineering practices within cloud-based analytics platforms.

Data Quality Engineering

Design and implement reusable data quality validation frameworks to ensure completeness, accuracy, consistency, timeliness, and compliance with business rules. Embed automated quality controls into data pipelines and monitoring processes. Identify, investigate, and resolve data quality issues that impact reporting and analytics.

Establish data quality metrics, monitoring standards, and remediation processes. Promote best practices that improve trust and reliability across enterprise data assets.

Data Engineering & Pipeline Development

Design, develop, and optimize scalable data pipelines using Databricks, Python, SQL, APIs, and cloud-native technologies. Build and maintain reliable data ingestion, transformation, and delivery processes. Support medallion architecture patterns and modern data engineering methodologies.

Ensure secure, efficient, and cost-effective movement of data across systems and platforms. Continuously improve pipeline performance, scalability, and operational reliability.

Data Modeling & Analytics Enablement

Design and maintain dimensional data models including star schemas and enterprise data warehouse structures. Develop trusted, reusable datasets that support analytics, reporting, and operational business processes. Translate business requirements into scalable data models and governed reporting assets.

Standardize definitions, metrics, and business rules to improve consistency across analytics solutions. Optimize data structures to improve reporting performance and ease of consumption. Technical Debt Reduction & Platform Improvement Assess existing solutions and identify opportunities to simplify, modernize, and standardize data architectures.

Reduce technical debt through improved engineering patterns, automation, and platform optimization. Refactor legacy pipelines and workflows to improve maintainability and supportability. Implement engineering standards that improve reliability and scalability.

Drive continuous improvement initiatives across the data ecosystem.

Data Lifecycle Management & Governance

Design and implement governed data retention, archival, and purge processes. Ensure compliance with data governance, security, audit, and retention requirements. Maintain data lineage, metadata, and documentation supporting enterprise governance practices.

Collaborate with stakeholders to establish standards for data ownership and stewardship. Support regulatory and operational compliance initiatives through effective data management practices.

Data Integration & Platform

Support Integrate data from APIs, enterprise applications, third-party platforms, and other source systems. Develop and maintain scalable data exchange and integration solutions. Support production operations, issue resolution, and ongoing platform maintenance.

Monitor platform health and proactively address performance or reliability concerns. Collaborate with engineering teams to support enterprise-scale data operations.

Documentation & Operational Readiness

Create and maintain technical documentation, architecture diagrams, data definitions, lineage artifacts, and support procedures. Document data quality rules, validation processes, and operating standards. Develop support handoff materials that enable long-term sustainment and operational excellence.

Ensure solutions are well-documented and audit-ready. Promote knowledge sharing across engineering and analytics teams.

Stakeholder Collaboration

Partner with business stakeholders, analysts, data engineers, and third-party teams to understand requirements and priorities. Translate business rules into governed technical solutions. Communicate risks, dependencies, and technical concepts to both technical and non-technical audiences.

Participate in project planning, solution design, and delivery activities. Support analytics initiatives through consultation, data analysis, and engineering expertise.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field, or equivalent practical experience.
• 4+ years of experience in Data Engineering, Analytics Engineering, Software Development, or related technical roles.
• 4+ years of experience designing, developing, and supporting production data solutions.
• Strong experience working with Databricks or Snowflake, with Databricks preferred.
• Proven experience building scalable data pipelines and enterprise data assets.
• Technical Skills Advanced proficiency in Python and SQL.
• Strong experience with Databricks and modern cloud-based data platforms.
• Data pipeline development and optimization.
• Data quality framework implementation and monitoring.
• ETL/ELT architecture and development.
• Dimensional modeling and star schema design.
• Data warehousing and enterprise data management.
• API integration and data exchange solutions.
• Data governance, lineage, retention, and audit processes.
• Production support and operational monitoring.
• Large-scale database management and query optimization.

Preferred Qualifications

• Experience working with large and complex enterprise data environments.
• Knowledge of Azure cloud services and cloud-native data solutions.
• Experience with project management and collaboration tools such as Jira or Asana.
• Familiarity with data visualization platforms including Power BI, Tableau, or Looker.
• Experience supporting healthcare, life sciences, or highly regulated industries.