Data Engineer

Analytics Engineer Duties and Responsibilities

• Contribute to the design and development of scalable reporting and analytics solutions that enable trusted business metrics and self-service reporting.

• Design and build data solutions using Databricks, SQL, Python, Spark, and Delta Lake within the Azure ecosystem (Blob Storage, Data Factory, Event Hubs).

• Develop, maintain, and optimize complex SQL queries, views, stored procedures, and reporting datasets supporting enterprise reporting platforms such as SSRS and Tableau.

• Design and implement dimensional data models, including fact and dimension tables, to support reporting, analytics, and KPI standardization.

• Collaborate with business stakeholders to understand reporting requirements and translate them into scalable data and reporting solutions.

• Support existing reporting solutions by troubleshooting production issues, implementing enhancements, and developing new reports, datasets, and data products.

• Participate in defining and standardizing business KPIs, metrics, and reporting logic across the organization.

• Contribute to the development of reusable transformation and semantic layers using modern analytics engineering practices (dbt or equivalent frameworks).

• Adhere to best practices for ETL/ELT development, including data quality, testing, maintainability, performance optimization, and reusability.

• Collaborate with engineers, product teams, and business leaders to ensure data platforms integrate effectively with reporting tools, APIs, and custom applications.

• Establish and follow CI/CD processes, testing frameworks, infrastructure-as-code practices, and monitoring solutions using Git, Terraform, Azure DevOps, GitHub Actions, Jenkins, Azure Monitor, or similar technologies.

• Support modernization initiatives including reporting platform upgrades, BI tool migrations, and analytics platform enhancements.

• Adhere to organizational security, compliance, and governance standards.

Qualifications Education

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.

• Advanced degree is a plus.

Experience

• 5+ years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or Data Warehousing.

• Strong experience with Microsoft SQL Server, PostgreSQL, MySQL, or similar relational database platforms.

• Hands-on experience developing and optimizing complex SQL queries, stored procedures, and reporting datasets.

• Experience supporting enterprise reporting platforms such as SSRS, Tableau, Power BI, or similar BI tools.

• Experience designing dimensional data models using Kimball methodology, including fact tables and dimension tables.

• Experience working directly with business users to gather requirements and deliver reporting and analytics solutions.

• Experience with Databricks, Delta Lake, and modern cloud-based data platforms.

• Experience with Azure data services such as Data Factory, Blob Storage, Synapse Analytics, and Event Hubs.

• Exposure to analytics engineering concepts, semantic layers, metrics frameworks, or dbt is highly desirable.

• Experience in healthcare, revenue cycle management, or other regulated industries is a plus.

Specific Job Knowledge, Skills, and Abilities

• Strong T-SQL development skills, including query optimization, execution plan analysis, indexing strategies, and stored procedure tuning.

• Strong programming skills in Python, Scala, or Java, with experience using PySpark, Pandas, or related frameworks.

• Strong understanding of data warehousing concepts, dimensional modeling, and analytics data architecture.

• Understanding of KPI frameworks, metrics definitions, semantic layers, and reusable reporting datasets.

• Experience with Databricks notebooks, Delta Lake, Unity Catalog, and modern ELT architectures.

• Familiarity with Git, CI/CD practices, Azure DevOps, GitHub Actions, or similar tooling.

• Ability to balance new development, operational support, and continuous improvement initiatives.

• Excellent communication and stakeholder management skills.

• Strong analytical and problem-solving abilities with a focus on delivering business value through data.