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 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 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/pharmadomain companies.
- Experience in designing and maintainingdata 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, PySparkor 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 or Marklogic or Neo4J or Allegrograph, etc.
Education and Professional Certifications
- 12 to 15 years of experience in Computer Science, IT or related field
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
- 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
