Job
Description
Roles & Responsibilities Lead and manage the enterprise data operations team responsible for data ingestion, processing, validation, quality control, and publishing Define and implement SOPs for data lifecycle management ensuring data accuracy, completeness, and integrity Oversee and improve daily operational workflows: scheduling, monitoring, troubleshooting data jobs across cloud and on-prem environments Establish and track key data operations metrics (SLAs, throughput, latency, data quality, incident resolution) and drive continuous improvement Partner with data engineering and platform teams to optimize pipelines, support integrations, and ensure scalability and resilience Collaborate with governance, compliance, and security teams to maintain regulatory compliance, data privacy, and access controls Serve as the 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 needs and prioritize operational goals Drive adoption of best practices for documentation, metadata, lineage, and change management Mentor and develop a high-performing team of data operations analysts and leads Must-Have Skills Experience managing data engineering teams in biotech/pharma domains Expertise in designing and maintaining ETL data pipelines and analytics solutions Hands-on experience with cloud platforms (AWS preferred) for scalable, cost-effective data solutions Experience managing data workflows on AWS, Azure, or GCP Strong problem-solving and analytical skills for complex data flow issues Proficiency in SQL, Python, or scripting for process monitoring and automation Collaboration experience across data engineering, analytics, IT ops, and business teams in matrix organizations Familiarity with data governance, metadata management, access control, and compliance frameworks (GDPR, HIPAA, SOX) Excellent leadership, communication, and stakeholder management skills Knowledge of full stack development, DataOps automation, logging frameworks, and pipeline orchestration tools Good-to-Have Skills Data Engineering management experience in Biotech/Life Sciences/Pharma Experience with graph databases (Stardog, Marklogic, Neo4J, Allegrograph) Education & Professional Certifications Doctorate degree with 3-5+ years experience OR Master's degree with 6-8+ years experience OR Bachelor's degree with 10-12+ years experience in Computer Science, IT, or related field AWS Certified Data Engineer (preferred) Databricks Certificate (preferred) Scaled Agile SAFe certification (preferred) Soft Skills Excellent analytical and troubleshooting skills Strong verbal and written communication Ability to work effectively with global, virtual teams High initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented mindset focused on achieving goals Strong presentation and public speaking skills
