Data Analyst II

Integrity Management Services, Inc. (IntegrityM) is an award-winning, women-owned small business specializing in assisting government and commercial clients in compliance and program integrity efforts, including the prevention and detection of fraud, waste, and abuse in government programs. Results are achieved through data analytics, technology solutions, audit, investigation, and medical review.

At IntegrityM, we offer a culture of opportunity, recognition, collaboration, and supporting our community. We thrive off of these fundamental elements that make IntegrityM a great place to work. Our small, flexible workplace offers an exceptional quality of life and promotes corporate-driven sustainability. We deliver creative solutions that exceed goals and foster a dynamic, idea-driven environment that nurtures our employees’ professional development.

In this role, the Data Analyst II will provide data engineering, analytics, and production support services through the design, development, maintenance, and optimization of Snowflake-based data solutions, ETL/ELT pipelines, reporting, and advanced analytics. The Data Analyst II will support data integration, data warehousing, and cloud-based data engineering initiatives while working independently and collaboratively with project stakeholders to develop high-quality, data-driven solutions.

Job Responsibilities:

Business Analysis and Data Modeling

• Design, develop, maintain, and optimize ETL (Extract, Transform, Load) processes to support data integration, validation, reporting, and analytics requirements.
• Conduct routine and advanced business analysis using statistical techniques, data mining, and explanatory or predictive modeling to support operational and strategic decisions.
• Build and apply complex analytical models and data solutions using tools such as Python, Snowflake, Databricks R, SQL, and SAS to address project-specific requirements and support operational decision-making.
• Research industry best practices and contribute to the development of data-driven solutions and actionable recommendations for improving business operations.
• Write, troubleshoot, and optimize complex SQL queries, stored procedures, views, and data pipelines across relational and cloud-based data platforms.
• Perform data profiling, cleansing, transformation, reconciliation, and quality assurance activities to ensure the accuracy and integrity of analytical datasets.
• Design, develop, and support data pipelines utilizing Snowflake features including Streams, Tasks, Time Travel, Snowsight Notebooks, and Streamlit applications.
• Monitor, troubleshoot, and resolve production data issues, conducting root cause analysis and implementing corrective actions.
• Develop and maintain automated data ingestion, validation, auditing, and reconciliation processes to ensure data integrity and reliability.
• Support CI/CD deployment activities, release management, and process automation initiatives.
• Collaborate with cloud engineering teams to support solutions leveraging AWS services including S3, Glue, and Airflow.

Client Engagement and Reporting

• Collaborate with internal and external clients to gather analytical requirements and deliver ad hoc reports and data products that meet defined objectives.
• Interpret business data and translate findings into clear, meaningful insights for diverse audiences, including technical and non-technical stakeholders.
• Collaborate with business users, data engineers, and technical stakeholders to define data requirements and implement scalable data ingestion, transformation, and reporting solutions.

Collaboration and Communication

• Participate in team projects and brainstorming sessions to evaluate methodologies and study designs that support program goals.
• Communicate analytical methods and results clearly, ensuring procedures and outcomes are well understood by both technical teams and end users.
• Research best practices and supports developing the solutions and recommendations for current business operations.
• Performs routine business analysis using various techniques (e.g., statistical analysis, explanatory and predictive modeling, data mining).