Engineering Manager

We are looking for a Data Engineering Manager – Pharma to help pharmaceutical clients solve complex data and analytics challenges and enable sustained analytics transformation. You will work closely with global pharma stakeholders to understand their strategic, clinical, commercial, and operational goals and translate them into scalable, compliant data engineering solutions.

This role requires a strong balance of technical depth, client-facing leadership, and people management, along with an understanding of pharma data landscapes and regulatory considerations. You will be expected to drive data-driven decision-making across engagements, institutionalize analytics capabilities, and build long-term client partnerships while continuously strengthening team and personal capabilities.

Key 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.
• Analyze 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 MathCo’s quality standards and client regulatory expectations.

Required Skills and Experience

• 8–12 years of overall experience in data engineering, analytics, or data platforms.
• 4+ 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, and uphold MathCo’s code of ethics and business conduct.
• 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).