Senior Engineering Manager - Data Operations

Senior Engineering Manager (Data Engineering)

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 the

  availability, 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 the 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 on

  Databricks

  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, PySpark

  , 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 & observability 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, Marklogic, Neo4J, or Allegrograph, etc.

Education and Professional Certifications

• 12 to 15 years of experience in Computer Science, IT, or a related field.

• Preferred

  : Databricks Certificate.

• Preferred

  : Scaled Agile SAFe certification.
• Experience in life sciences, healthcare, or other regulated industries with large-scale operational data environments.
• Familiarity with

  incident and change management processes (e.g., ITIL)

  .

Soft Skills

• Excellent analytical and troubleshooting skills.
• 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.