Data Engineering Manager

Responsibilities

• Lead and manage teams to design, develop, and deliver high-quality data engineering solutions for pharma and life sciences clients.
• Own end-to-end delivery of data engineering engagements, ensuring solutions meet client expectations on quality, timelines, compliance, and business impact.
• Design, review, and guide scalable data architectures, pipelines, and data models across clinical, commercial, R&D, and real-world data domains.
• Manage client engagements and relationships, including requirement discovery, expectation management, and handling escalations in regulated environments.
• Translate client strategies and business goals into actionable data platforms, pipelines, and analytics-ready datasets across ongoing and future projects.
• Institutionalize data-driven insights and enable advanced analytics and AI use cases across pharma functions.
• Use data and insights to inform conclusions, recommendations, and decision-making for client leadership teams.
• Analyse complex, ambiguous problems by synthesizing multiple data sources (internal systems, third-party data, and stakeholder inputs) into clear, meaningful recommendations.
• Build consensus across diverse stakeholder groups, including business, IT, compliance, and analytics teams.
• Actively identify and resolve delivery, quality, data integrity, or execution issues that prevent teams from working effectively.
• Address substandard work and ensure outputs meet MathCos quality standards and client regulatory expectations.

Qualifications:

• 812+ years of overall experience in data engineering, analytics, or data platforms.
• 3+ years of experience leading teams and managing end-to-end client engagements.
• Prior experience working with pharma or life sciences clients in a consulting or enterprise environment.
• Strong hands-on experience with Python and SQL.
• Deep expertise in Spark / PySpark and distributed data processing.
• Experience building and managing ETL/ELT pipelines, orchestration workflows, and analytics-ready data platforms.
• Solid understanding of data modelling, performance optimization, and scalable data architectures.
• Must have a strong technical background with hands-on exposure to at least two of the following: AWS, Databricks, GenAI, or Snowflake along with a solid understanding of programming.
• Must have worked on pharma domain - preferably among patient services, market access, marketing, HCP or rep focused use cases.
• Strong analytical and problem-solving skills with the ability to analyse complex ideas and develop structured recommendations.
• Strong communication skills with proven experience in stakeholder management.
• Ability to leverage multiple sources of information, including stakeholder perspectives, to develop solutions.
• Must have led teams of at least 15 members. Ability to coach others, recognize strengths, encourage ownership of development.
• Ability to develop a point of view on industry and global trends and articulate their impact on pharma clients.
• Understanding of data governance, privacy, and compliance considerations (e.g., GxP, data quality, auditability).