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.

Increase your chances of reaching the interview stage by reading the complete job description and applying promptly.

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 a highly regulated industry (life sciences, pharmaceuticals, healthcare).

Strong expertise with data integration tools (e.g., Microsoft Azure Data Factory, Informatica), ETL processes, and data governance platforms (e.g., Collibra, Alation).

Demonstrated experience as a self-directed technical contributor with hands-on expertise in data engineering, data governance, and compliance automation, including work with LabVantage LIMS and ERP systems.

Proven ability to define data standards, develop and implement quality assurance processes, and ensure data integrity in alignment with regulatory frameworks such as HIPAA, GxP, GDPR, and FERPA.

Preferred Technical Skills

Proficiency in designing, building, and optimizing ETL pipelines using SQL and cloud data tools (Azure Data Factory, Informatica). Ability to work with large datasets from multiple sources (LIMS, ERP).

Experience with RPA platforms (e.g., UiPath, Microsoft Power Automate) to independently automate data validation checks and routine compliance tasks.

Skilled in developing and managing APIs for data integration, enabling seamless connectivity between lab systems, ERP, and data hubs.

Proficient in Power BI or Tableau for creating dashboards and visualizations, translating data insights into accessible formats for lab and operational teams.

Familiarity with governance platforms like Collibra or Alation for data cataloging, quality monitoring, and compliance tracking.

Preferred Minimum Skills

Ability to execute on all technical aspects of data integration, automation, and quality independently, handling complex data challenges in a dynamic lab environment.

Skilled at defining strategic goals and translating them into detailed, tactical execution plans.

Eagerness to develop new capabilities such as the data management function scales; adaptable and open to taking on greater leadership responsibilities.

Strong ability to communicate technical concepts clearly and work cross-functionally to align data management goals with BioAgilytix’s operational and regulatory requirements

Demonstrated ability to work autonomously on complex data projects, with hands-on skills across data engineering, automation, and governance.

Skilled at diagnosing data challenges and designing innovative solutions to improve data quality, integration, and regulatory adherence.

Capable of growing with the role and developing the data management team in alignment with the IT and Data Strategy.

Able to navigate a fast-paced, evolving data landscape, demonstrating resilience and flexibility in the face of new challenges.

Position Type and Expected Hours of Work

This is a full-time position

Some flexibility in hours is allowed, but the employee must be available during the “core” work hours as published in the BioAgilytix Employee Handbook

BENEFITS AND OTHER PERKS

Medical Insurance (HDHP with HSA; PPO), Dental Insurance, Vision Insurance, Flexible Spending Account (medical; dependent care), Short Term Disability \u007C Long Term Disability Life Insurance, Paid Time Off (4 weeks per year), Parental Leave, Paid Holidays (9 scheduled; 5 floating), 401k with Employer Match, Employee Referral Program

COMMITMENT TO EQUAL OPPORTUNITY

BioAgilytix provides equal employment opportunities to all employees and applicants for employment without regard to race, color, ancestry, national origin, gender, sexual orientation, marital status, religion, age, disability, gender identity, results of genetic testing, service in the military, or any other group protected by federal, state, or local law.