Job
Description
You will lead the end-to-end management of enterprise data assets and operational data workflows. This critical role ensures availability, quality, consistency, and timeliness of data across platforms and functions, supporting analytics, reporting, compliance, and digital transformation initiatives. You will manage day-to-day data operations, lead a team of data professionals, and drive process excellence across data intake, transformation, validation, and delivery. Close collaboration with data engineering, analytics, IT, governance, and business stakeholders is essential to align operational data capabilities with enterprise needs. Roles & Responsibilities Lead and manage the enterprise data operations team responsible for data ingestion, processing, validation, quality control, and publishing to downstream systems. Define and implement standard operating procedures for data lifecycle management, ensuring data accuracy, completeness, and integrity. Oversee and continuously improve daily operational workflows including scheduling, monitoring, and troubleshooting data jobs in cloud and on-prem environments. Establish and track key data operations metrics (SLAs, throughput, latency, data quality, incident resolution) and drive ongoing improvements. Partner with data engineering and platform teams to optimize pipelines, support new data integrations, and ensure scalable, resilient operational data flows. Collaborate with data governance, compliance, and security teams to maintain regulatory compliance, data privacy, and access controls. Act as primary escalation point for data incidents and outages, ensuring rapid response and root cause analysis. Build strong relationships with business and analytics teams to understand data consumption, prioritize operational needs, and align with business objectives. Drive adoption of best practices for documentation, metadata, lineage, and change management across data operations. Mentor and develop a high-performing team of data operations analysts and leads. Must-Have Functional Skills Experience managing a team of data engineers in biotech/pharma companies. Expertise in designing and maintaining ETL data pipelines and analytics solutions from multiple sources. Hands-on experience with cloud platforms (especially AWS), including architecting scalable, cost-effective data solutions. Skilled in managing data workflows in cloud environments such as AWS, Azure, or GCP. Strong problem-solving skills to analyze complex data flow issues and implement sustainable solutions. Proficiency in SQL, Python, or scripting languages for monitoring and automation. Experience collaborating in matrixed organizations with data engineering, analytics, IT operations, and business teams. Familiarity with data governance, metadata management, access controls, and regulatory standards (e.g., GDPR, HIPAA, SOX). Excellent leadership, communication, and stakeholder engagement skills. Knowledgeable in full stack development, DataOps automation, logging frameworks, and pipeline orchestration tools. Strong analytical and interpersonal communication abilities. Good-to-Have Skills Data Engineering management experience in Biotech/Life Sciences/Pharma sectors. Experience with graph databases such as Stardog, Marklogic, Neo4J, or Allegrograph. Education and Professional Certifications 9 to 12 years of experience in Computer Science, IT, or related fields. AWS Certified Data Engineer (preferred). Databricks Certificate (preferred). Scaled Agile SAFe certification (preferred). Soft Skills Excellent analytical and troubleshooting capabilities. Strong verbal and written communication skills. Ability to work effectively with global, virtual teams. High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented with a focus on achieving team goals. Strong presentation and public speaking skills.
