Engineering Manager

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 accuracy, completeness, and integrity of critical data assets. Oversee and continuously improve daily operational workflows including scheduling, monitoring, and troubleshooting data jobs across cloud and on-premise environments. Establish and track key data operations metrics such as SLAs, throughput, latency, data quality, and incident resolution, driving continuous improvements. Partner with data engineering and platform teams to optimize pipelines, support new data integrations, and ensure scalability and resilience of data flows. Collaborate with data 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 consumption patterns, 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. Functional Skills Must-Have Skills: Experience managing data engineering teams in biotech/pharma domains. Designing and maintaining ETL/ELT pipelines from multiple source systems. Hands-on experience with cloud platforms (especially AWS) for scalable and cost-effective data solutions. Managing data workflows in cloud environments like AWS, Azure, or GCP. Strong problem-solving skills for complex data flow issues with sustainable solutions. Proficiency in SQL, Python, or scripting languages for automation and monitoring. Experience collaborating in matrixed organizations with data engineering, analytics, IT, and business teams. Familiarity with data governance, metadata management, access controls, and regulatory requirements (GDPR, HIPAA, SOX). Excellent leadership, communication, and stakeholder engagement skills. Knowledge of full-stack development, DataOps automation, logging frameworks, and pipeline orchestration tools. Good-to-Have Skills: Data engineering management experience in Life Sciences/Pharma. Experience with graph databases like 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 Framework (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 initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented with focus on achieving goals. Strong presentation and public speaking skills.