Manager Analytics Engineering [T500-25822]

ANSR is hiring for one of its clients.

About ANSR MedTech:

Who We Are:

ANSR MedTech Capability Center is a new global innovation hub being established in India for a Fortune 100 Fastest-Growing Company in the MedTech sector. Built in partnership with ANSR, the center draws on ANSRs proven experience in establishing and scaling high-performance Global Capability Centers (GCCs) for leading global enterprises.

ANSR MedTech center brings together world-class engineering, product, and technology talent to build next-generation healthcare platforms and solutions that power global operations.

Our Vision:

To build a next-generation MedTech capability center that powers global healthcare innovation. We envision:

• High-impact innovation hubs shaping global product and technology roadmaps
• Centers that go beyond support functions to drive core engineering and platform development
• Sustainable, scalable ecosystems that nurture world-class MedTech talent
• Capability centers that directly influence patient outcomes worldwide.

At its core, the ANSR MedTech Capability Center is about enabling innovation that touches lives at scale.

About IACTO:

At ANSR MedTech, the Insights & Analytics (I&A) Center of Excellence is built as a single, integrated analytics organization with two closely partnered focus areas: Growth aligned analytics and CTO aligned analytics.

Both work together to ensure analytics drives real outcomes at enterprise scale.

Growth Aligned Insights & Analytics:

Growth aligned I&A teams focus on turning data into decisions.

They work closely with business leaders to understand context, analyze data, and deliver insights that directly inform commercial, product, and operational actions.

These roles are best suited for people who enjoy:

• Working close to the business and stakeholders
• Translating complex data into clear insights and recommendations
• Driving measurable impact through analytics.

CTO Aligned Insights & Analytics:

CTO aligned I&A teams focus on building and operating the data and AI foundation that enables analytics at scale.

They design, build, and maintain platforms, pipelines, models, and governance that ensure analytics is secure, reliable, and repeatable.

These roles are best suited for people who enjoy:

• Building scalable data platforms and AI capabilities
• Engineering high quality, production ready data solutions
• Establishing standards, reliability, and long-term sustainability.

How the Teams Work Together:

• Growth defines what insights matter and how they are used
• CTO enables how those insights are delivered at scale
• Teams operate as one connected analytics organization, with clear ownership and shared outcomes

This model allows ANSR MedTech to deliver fast, meaningful insights while building a strong, enterprise grade analytics foundation for long term growth.

Job Summary:

The Manager of Analytics Engineering will lead a team of analytics engineers within the India COE, accountable for the design, development, and delivery of production-grade dashboards, enterprise reporting, semantic layers, and self-service analytics capabilities that enable data-driven decision-making across the organization.

This role reports to the Director of Data & AI and operates within a multi-disciplinary delivery organization collaborating closely with data scientists, AI engineers, data engineers, and data governance professionals to deliver end-to-end analytical products.

Key Responsibilities:

• Lead the design, development, and delivery of enterprise dashboards, operational reporting, executive scorecards, and ad-hoc analytical views across business functions
• Establish visualization standards, information design principles, and accessibility guidelines for all COE-produced reporting
• Define and enforce a consistent visual language across all dashboards including color palettes, layout templates, interaction patterns, and drill-through standards
• Ensure every dashboard is documented with data sources, refresh schedules, metric definitions, and known limitations before production release
• Collaborate with Data Product Owners and Business Analysts to translate business requirements into effective, intuitive, and actionable reporting
• Design and implement the semantic layer standardized business logic, metric definitions, and governed data models that ensure consistent reporting across teams and geographies
• Develop and maintain data transformation pipelines that convert raw source data into analytics-ready datasets with documentation, testing, and version control
• Establish naming conventions, modeling standards, and transformation patterns that ensure consistency and reusability across the analytics engineering team
• Collaborate with data engineers to define the interface between data platform layers (bronze/silver/gold) and analytics consumption
• Design and maintain a self-service analytics program: governed datasets, curated data products, and training materials that empower business users to explore data independently
• Define guardrails for self-service usage ensuring business users work with certified datasets and governed tools without compromising data quality or security
• Monitor self-service adoption, usage patterns, and support requests to continuously improve the self-service experience
• Recruit, develop, and lead a team of analytics engineers and BI developers at varying experience levels
• Conduct regular code reviews, design reviews, and peer learning sessions to maintain quality and share best practices
• Define skill requirements for each level and create clear expectations for progression from associate to senior analytics engineer
• Foster a culture of craftsmanship, attention to detail, and user-centered design where every deliverable is built with the end user in mind
• Define and enforce coding standards, peer review processes, quality gates, and definition-of-done criteria for analytics engineering deliverables consistent with how ANSR MedTechs established Data & AI teams operate
• Ensure all dashboards and data models are version-controlled, tested, documented, and certified through the data product certification process before release
• Contribute to the COEs library of reusable dashboard templates, transformation patterns, and semantic layer components
• Implement automated testing and validation for data transformations and reporting logic
• Partner with Data Product Owners and Business Analysts to ensure deliverables align with business requirements and acceptance criteria
• Collaborate with peer managers across data science, analytics engineering, AI engineering, and data engineering to share standards and best practices
• Participate in sprint planning and capacity alignment with the I&A organization
• Contribute to cross-functional reviews of delivery metrics and business impact.

Qualifications:

• Masters degree or above in computer science, data science, information systems, engineering, or a related quantitative field
• 712+ years of hands-on experience in analytics engineering, business intelligence, or data analytics with at least 3 years leading technical teams
• Strong expertise in BI platforms (Power BI or Databricks SQL/Dashboards) and SQL-based data transformation
• Experience designing and maintaining semantic layers, governed data models, and enterprise reporting standards
• Proficiency in SQL and at least one transformation framework (dbt, Databricks notebooks, or equivalent)
• Experience building self-service analytics programs with governed datasets and user enablement
• Strong understanding of data modeling (star schema, snowflake, OBT), slowly changing dimensions, and incremental processing patterns
• Experience working in agile delivery teams with data scientists, data engineers, and product owners
• Excellent communication skills with the ability to translate business requirements into effective data visualizations.

Preferred skills:

• Experience in medtech, life sciences, healthcare, or other regulated industries
• Experience with Databricks (SQL, Unity Catalog, Dashboards) and Azure ecosystem
• Familiarity with data governance tooling: Atlan, Alation, or equivalent data catalog platforms
• Experience with accessibility standards for data visualization (WCAG compliance)
• Understanding of data engineering practices: CI/CD for pipelines, orchestration, data quality monitoring.