Data Management Lead

This role is BioAgilytix’s go-to person for everything related to data. You’ll build the rules, processes, and cloud pipelines that keep lab and business information accurate, secure, and easy to find. That means hands-on work designing Azure Data Factory and API integrations, automating data-quality checks, and setting up clear governance so everyone knows who can see or change each dataset. You’ll start as a “player-coach,” doing the technical heavy lifting yourself while laying out the roadmap for a future team.
Just as important, you’ll own the last mile: turning those clean, validated data streams into the audit-ready files our pharmaceutical sponsors need on tight deadlines. You’ll harmonize results from multiple sites, package them in industry-standard formats, and automate the hand-offs so exports ship in hours rather than days. Over time, the same foundation you build will support dashboards, predictive analytics, and other data-driven tools that boost lab productivity and keep BioAgilytix ahead of regulatory requirements.

Key Responsibilities

• Data Governance & Quality Standards:
• Develop a foundational data governance framework, with hands-on responsibility for data quality, access control, and compliance protocols.
• Define standards, build quality assurance processes, and monitor data integrity to meet regulatory requirements, including HIPAA, GxP, GDPR, and FIRPA.
• Design data quality KPIs and implement initial quality checks independently, ensuring data accuracy, consistency, and reliability.
• Data Integration & Infrastructure Development:
• Develop ETL processes to automate data extraction, transformation, and loading from core systems (LabVantage LIMS, ERP) into a centralized data repository.
• Design and implement the architecture for a scalable data hub, using cloud platforms like Microsoft Azure or SQL-based solutions for centralized data storage.
• Create APIs to enable real-time data sharing between systems, with a focus on operational efficiency and data integrity. 
• Data Quality & Compliance Automation:
• Use Robotic Process Automation (RPA) tools like UiPath or Microsoft Power Automate to automate high-frequency, repetitive tasks, such as data validation and QC compliance checks.
• Establish automated data validation and rule-based checks to identify data discrepancies in real-time, enhancing regulatory compliance and minimizing errors.
• Independently design and configure basic automated workflows to streamline compliance reporting, reducing manual efforts.
• Technical Roadmap for Data Analytics and Predictive Capabilities:
• Organize and clean historical QC and operational data to lay the groundwork for future predictive models focused on quality control, lab maintenance, and operational forecasting.
• Assess core lab and operational data for predictive value, setting up structured data sets to support the rollout of predictive analytics as the team grows.
• Independently create data visualizations and dashboards using tools like Power BI or Tableau to enable lab and operations teams to access actionable insights.
• Cross-Functional Collaboration and Future Team Development:
• Work closely with IT, lab operations, and quality/compliance teams to ensure alignment on data goals and processes.
• Act as an advocate for data quality and governance, providing education to teams on the importance of data integrity and compliance.
• Develop a roadmap for building a high-performing data management team, identifying roles in data engineering, quality, and automation as the function matures.
• Sponsor Data Delivery & Harmonization
• Own End-to-End Sponsor Data Delivery: Direct the complete pipeline for extracting, transforming, validating, packaging, and securely transmitting data sets to sponsors, meeting agreed SLAs (e.g., ≤ 24 h for interim cuts, ≤ 5 days for final locked data).
• Harmonize Multi-Site, Multi-Assay Data: Standardize fields, units, and controlled vocabularies (e.g., CDISC SDTM/SEND) to present a single, consistent dataset to sponsors.
• Automate Delivery Workflows: Leverage RPA, scripted edit checks, and audit-trail capture to shrink cycle times and reduce defects.
• Enable Self-Service Access: Provide dashboards, secure APIs, or portals that allow sponsors real-time visibility into validated data.
• Maintain Audit-Ready Traceability: Ensure comprehensive data-lineage and validation documentation is always ready for FDA, EMA, or sponsor inspection.

Minimum Preferred Qualifications

• Bachelor’s degree in data science, Information Management, Computer Science, or related field; master’s degree preferred.
• 10+ years in data management, governance, or a related field, ideally in