As a Data Engineering Manager, you will be a strategic leader responsible for the development, optimization, and maintenance of scalable data architectures and pipelines. You will lead a team of data engineers with deep expertise in the R&D domain of the biotech/pharma industry. This role requires technical proficiency, strong leadership, and a commitment to driving best practices in data engineering, cloud technologies, and Agile methodologies to support real-time analytics and AI-driven insights.
Roles & Responsibilities
- Leadership & Mentorship: Lead and mentor a team of data engineers, fostering a culture of innovation and continuous learning. You will guide your team in writing high-quality code and validating data ingestion, processing, and transformation techniques.
- Architecture & Pipelines: You will oversee the development of data extraction, validation, and transformation techniques. This includes driving the development of data tools and frameworks and overseeing the implementation of performance monitoring protocols to ensure pipeline stability and efficiency.
- Governance & Best Practices: Oversee data governance strategies, ensuring compliance with security policies and regulations. You will guide engineers in data modeling, metadata management, and access control. You will also stay up-to-date with emerging data technologies and industry trends.
- Agile & Strategic Collaboration: Drive Agile and Scaled Agile (SAFe) methodologies, handling sprint backlogs and prioritization. You will collaborate with business leaders, product owners, and cross-functional teams to align data architecture with business objectives. You will also prepare your team for key partner discussions by assessing data costs and requirements.
Qualifications
- A Master's degree with 4-6 years of experience, a Bachelor's degree with 6-8 years of experience, or a Diploma with 10-12 years of experience in Computer Science, IT, or a related field. A Doctorate degree is also accepted.
- Experience leading a team of data engineers in the R&D domain of biotech/pharma companies.
- Demonstrated hands-on experience with cloud platforms (AWS, GCP, or Azure) and the ability to architect cost-effective and scalable data solutions.
- Proficiency in Python, PySpark, and SQL.
- Experience with dimensional data modeling and working with tools like Apache Spark and Apache Airflow.
- Experience with software engineering best practices, including version control (Git), CI/CD, and DevOps.
- Certifications such as AWS Certified Data Engineer, Databricks Certificate, or Scaled Agile SAFe are a plus.
Soft Skills
- Leadership: Strong leadership, presentation, and public speaking skills, with the ability to handle multiple priorities successfully.
- Problem-Solving: Strong analytical and problem-solving skills to address complex data challenges.
- Communication: Excellent verbal and written communication skills to collaborate effectively with cross-functional and global teams.
- Initiative: A high degree of initiative and self-motivation, with a focus on achieving team goals.
