Engineering Manager

Roles & Responsibilities:

Lead and manage the enterprise data operations team, responsible for data ingestion, processing, validation, quality control, and publishing to various 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 (SLAs, throughput, latency, data quality, incident resolution) and drive continuous improvements.

Partner with data engineering and platform teams to optimize pipelines, support new data integrations, and ensure scalability and resilience of operational 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 processes.

Mentor and develop a high-performing team of data operations analysts and leads.

Functional

Skills:

Must-Have Skills:

• Experience managing a team of data engineers in biotech/pharma domain companies.
• Experience in designing and maintaining data pipelines and analytics solutions that extract, transform, and load data from multiple source systems.
• Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions.
• Experience managing data workflows in cloud environments such as AWS, Azure, or GCP.
• Strong problem-solving skills with the ability to analyze complex data flow issues and implement sustainable solutions.
• Working knowledge of SQL, Python, or scripting languages for process monitoring and automation.
• Experience collaborating with data engineering, analytics, IT operations, and business teams in a matrixed organization.
• Familiarity with data governance, metadata management, access control, and regulatory requirements (e.g., GDPR, HIPAA, SOX).
• Excellent leadership, communication, and stakeholder engagement skills.
• Well versed with full stack development & DataOps automation, logging frameworks, and pipeline orchestration tools.
• Strong analytical and problem-solving skills to address complex data challenges.
• Effective communication and interpersonal skills to collaborate with cross-functional teams.

Good-to-Have Skills:

• Data Engineering Management experience in Biotech/Life Sciences/Pharma
• Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc.

Education and Professional Certifications

• Doctorate Degree with 3-5 + years of experience in Computer Science, IT or related field
• OR
• Master's degree with 6 - 8 + years of experience in Computer Science, IT or related field
• OR
• Bachelor's degree with 10 - 12 + years of experience in Computer Science, IT or related field
• AWS Certified Data Engineer preferred
• Databricks Certificate preferred
• Scaled Agile SAFe certification preferred