Analytics Engineer

• Maintain and extend existing data mart builds: claims aggregation, financial metrics, clinical metrics, and curated analytical tables that power Innovu’s SaaS platform
• Author, test, and maintain metric SQL templates against Innovu’s canonical data model (medical claims, pharmacy claims, eligibility, provider, plan design)
• Build and maintain data pipelines in Python and SQL (Redshift, PostgreSQL, S3-based integrations)
• Profile and validate third-party data feeds during vendor onboarding (new data sources land regularly)
• Investigate data quality issues from warehouse transformation logic through final output
• Produce analytical deliverables for clients and internal stakeholders (PMPM trending, cohort analysis, cost drivers)
• Write clear documentation for metric definitions, data lineage, assumptions, and known limitations
• Participate in monthly rotation for support triage (incoming tickets), release management, and mart build monitoring
• Participate in code reviews and contribute to team standards

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Data Analytics Engineer

• Develop and maintain ETL processes to support data ingestion, transformation, and integration.
• Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing.
• Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi.
• Write clean, efficient, and maintainable code using Python, C#, or Java.
• Ensure data quality, accuracy, and integrity across ETL pipelines.
• Collaborate with data engineers and analysts to define and refine ETL requirements.
• Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact.
• Follow test-driven development (TDD) methodologies to improve reliability and maintainability.
• Maintain and document ETL processes, providing insights and recommendations for improvements.

Associate ETL Engineer

• Database management: Maintain, optimize, and enhance our existing data infrastructure ensuring high performance and reliability.
• Data transformation: Develop and optimize ETL processes, ensuring accurate and timely data transformation.
• SQL: Write complex SQL queries to extract, transform, and analyze data from multiple sources.
• Data Literacy: Understand business context and support analysts/stakeholders in crafting and standardizing source-of-truth data models/visualizations
• Best Practices: Promote best practices for data management and ensure adherence to data governance standards.
• Documentation: Document technical requirements, specifications, and solutions.

Senior Analytics Engineer

• Design and implement maintainable analytics and ML pipelines for genomics and multi-omics data
• Implement state-of-the-art tools for analysing, interpreting, and visualizing genetic and genomic data
• Translate scientific and domain requirements into reliable, testable software systems
• Own workflows from prototype through to usable, shareable capability
• Apply machine-learning methods to answer biological questions
• Contribute engineering best practices within a scientific environment
• Act as a technical bridge between genomics scientists and AI/ML or platform teams

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Analytics Engineer

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Senior Analytics Engineer

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