Roles & Responsibilities:
- Lead and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning for solving complex problems of the R&D division.
- Oversee the development of data extraction, validation, and transformation techniques, ensuring ingested data is of high quality and compatible with downstream systems.
- Guide the team in writing and validating high-quality code for data ingestion, processing, and transformation, ensuring resiliency and fault tolerance.
- Drive the development of data tools and frameworks for running and accessing data efficiently across the organization.
- Oversee the implementation of performance monitoring protocols across data pipelines, ensuring real-time visibility, alerts, and automated recovery mechanisms.
- Coach engineers in building dashboards and aggregations to monitor pipeline health and detect inefficiencies, ensuring optimal performance and cost-effectiveness.
- Lead the implementation of self-healing solutions, reducing failure points and improving pipeline stability and efficiency across multiple product features.
- Oversee data governance strategies, ensuring compliance with security policies, regulations, and data accessibility best practices.
- Guide engineers in data modeling, metadata management, and access control, ensuring structured data handling across various business use cases.
- Collaborate with business leaders, product owners, and cross-functional teams to ensure alignment of data architecture with product requirements and business objectives.
- Prepare team members for key partner discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios.
- Drive Agile and Scaled Agile (SAFe) methodologies, handling sprint backlogs, prioritization, and iterative improvements to enhance team velocity and project delivery.
- Stay up-to-date with emerging data technologies, industry trends, and best practices, ensuring the organization uses the latest innovations in data engineering and architecture.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. We are seeking a seasoned Engineering Manager (Data Engineering) to drive the development and implementation of our data strategy with deep expertise in the R&D of Biotech or Pharma domain.
Basic Qualifications:
- Doctorate degree
- OR
- Master's degree and 4 to 6 years of experience in Computer Science, IT or related field
- OR
- Bachelor's degree and 6 to 8 years of experience in Computer Science, IT or related field
- OR
- Diploma and 10 to 12 years of experience in Computer Science, IT or related field
- Experience leading a team of data engineers in the R&D domain of biotech/pharma companies.
- Experience architecting and building data and analytics solutions that extract, transform, and load data from multiple source systems.
- Data Engineering experience in R&D for Biotechnology or Pharma industry
- Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions.
- Proficiency in Python, PySpark, SQL
- Experience with dimensional data modeling
- Experience working with Apache Spark, Apache Airflow
- Experienced with software engineering best practices, including but not limited to version control (Git, Subversion), CI/CD (Jenkins, Maven), automated unit testing, and DevOps
- Experienced with AWS or GCP or Azure cloud services
- Understanding of end-to-end project/product life cycle
- 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
Preferred Qualifications:
- AWS Certified Data Engineer (preferred)
- Databricks Certificate (preferred)
- Scaled Agile SAFe certification (preferred)
- Project Management certifications (preferred)
- Data Engineering Management experience in Biotech/Pharma is a plus
- Experience using graph databases such as Stardog, MarkLogic, Neo4J, or AllegroGraph
