Data Engineering

As a Life Sciences Data Engineering Technical Manager, you will lead data engineering initiatives to deliver innovative solutions supporting life sciences research, clinical trials, and healthcare analytics. You will manage a technical team, architect scalable data platforms, and ensure the delivery of high-quality data-driven solutions that align with organizational objectives and regulatory requirements.

Key Responsibilities:

• Technical Leadership:
  • Lead the design and development of robust, scalable, and efficient data pipelines and platforms to support life sciences applications.
  • Provide technical guidance and mentorship to a team of data engineers and developers.
• Data Architecture & Strategy:
  • Architect data solutions that integrate structured and unstructured data from diverse sources, including genomic data, clinical trials, and IoT medical devices.
  • Collaborate with data scientists, business analysts, and stakeholders to define data management strategies and support analytical modeling.
• Project Management:
  • Oversee project lifecycles, ensuring timely and successful delivery of data engineering projects.
  • Manage resources, project scope, timelines, and budgets while adhering to quality standards.
• Data Quality & Compliance:
  • Implement data governance practices, ensuring data quality, integrity, security, and compliance with industry regulations (e.g., HIPAA, GDPR).
  • Establish automated testing frameworks and monitoring tools for data systems.
• Performance Optimization:
  • Continuously evaluate and optimize system performance, ensuring minimal downtime and maximum efficiency.
• Stakeholder Collaboration:
  • Act as a liaison between technical teams and business leaders to align technical solutions with business needs.

Required Qualifications:

• Education:
  • Bachelor’s or Master’s degree in Computer Science, Data Engineering, Bioinformatics, or related field.
• Experience:
  • 8+ years of experience in data engineering, with 3+ years in a leadership role.
  • Proven track record in life sciences, healthcare, or pharmaceutical industries.
• Technical Skills:
  • Expertise in data engineering tools and technologies (e.g., Apache Spark, Hadoop, Airflow).
  • Strong proficiency in cloud platforms (AWS, Azure, GCP) and distributed data systems.
  • Advanced knowledge of programming languages such as Python, Scala, or Java.
  • Experience with database technologies (SQL, NoSQL) and data warehousing (Snowflake, Redshift)