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Chennai Corporation, Tamil Nadu, India
Onsite
Use Your Power for Purpose
Do you want to make a global impact on patient health? Do you thrive in a fast-paced environment that integrates scientific, clinical, and commercial domains through engineering, data science, and AI. Join Pfizer Digital’s Commercial Creation Center & CDI organization (C4) to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues, patients, and physicians. Our team of engineering, data science, and AI professionals is at the forefront of Pfizer’s transformation into a digitally driven organization, using data science and AI to change patients’ lives, leading process and engineering innovations to advance AI and data science applications from prototypes and MVPs to full production.
As a As a Commercial AI Analytics Solutions & Engineering Senior Manager, your responsibilities will include architecting and implementing AI solutions at scale for Pfizer. You will iteratively develop and continuously improve data science workflows, AI based software solutions, and AI components.
What You Will Achieve
DataOps & Analytics Platform Execution
Lead the design, build, and operation of data and analytics platforms supporting commercial reporting, advanced analytics, and AI/ML use cases.
Own operational pipelines for batch and streaming data ingestion, transformation, and serving, ensuring reliability, scalability, and performance.
Implement and maintain DataOps automation using CI/CD, infrastructure-as-code, and configuration management to support analytics and ML workloads.
Partner with infrastructure and platform teams to ensure data platforms are deployed using standardized cloud-native patterns (AWS/Azure).
Translate Director-level analytics platform strategy into working, production-grade data systems.
Data Reliability, Quality & Observability
Own end-to-end data reliability, including freshness, completeness, accuracy, and avalability across analytics and AI pipelines.
Implement data observability and monitoring capabilities (e.g., pipeline health, schema drift, SLA/SLO tracking).
Define and track data reliability KPIs, such as pipeline failure rates, data incident frequency, and recovery time.
Lead response to data incidents, including root-cause analysis, remediation plans, and post-incident reviews.
Drive adoption of data reliability engineering (DRE) and SRE-inspired practices within DataOps teams.
Testing & Quality Enablement for Data Pipelines
Define and enforce data testing standards, including:
Data quality checks (schema, nulls, ranges, distributions)
Pipeline validation and reconciliation
Regression testing for analytics transformations
Embed automated data tests into CI/CD workflows to support shift-left DataOps practices.
Partner with analytics, ML, and QA teams to support non-functional testing such as:
Performance and scalability of data pipelines
Reliability under load and failure scenarios
Track and report data quality and defect escape metrics, using insights to drive continuous improvement.
AI & Advanced Analytics Enablement
Enable data scientists and ML engineers by ensuring trusted, well-governed, and production-ready data assets.
Support operational analytics and AI workflows by providing:
Reliable feature pipelines
Versioned and reproducible datasets
Secure access to structured and unstructured data
Partner with AI and analytics leaders to support MLOps integration points, such as:
Data lineage for model training
Monitoring of data drift and input quality
Contribute to data governance standards for lineage, traceability, and stewardship across analytics lifecycles.
People Leadership & Ways of Working
Coach engineers on:
Data pipeline design and optimization
Automation and reliability practices
Secure and compliant data handling
Establish strong engineering discipline through design reviews, data contracts, documentation, and operational runbooks.
Partner closely with product, analytics, AI, and infrastructure leaders to sequence delivery and manage trade-offs.
Here Is What You Need (Minimum Requirements)
8+ years of experience in data engineering, analytics engineering, or DataOps roles.
Strong hands-on experience building and operating production data pipelines in AWS or Azure environments.
Proven expertise in:
Modern data processing frameworks (e.g., Spark, SQL-based transformation tools)
CI/CD and automation for data platforms
Data pipeline orchestration and monitoring
Solid understanding of testing and quality practices for data systems, including:
Automated data quality testing
Pipeline validation and regression testing
Supporting non-functional testing (performance, reliability, scalability)
Experience implementing data observability, monitoring, and incident management practices.
Demonstrated experience with secure data handling and governance, including access control and compliance-aware environments.
Proficiency in programming and scripting (e.g., Python, SQL, Scala, Bash).
Strong communication skills and ability to influence cross-functional teams and deliver outcomes through others.
Bonus Points If You Have (Preferred Requirements)
Master’s degree in Computer Science, Data Engineering, Analytics, or related field.
Experience supporting AI/ML workloads and feature pipelines in production.
Familiarity with MLOps concepts related to data (e.g., training data lineage, drift detection).
Background in data reliability engineering, SRE, or large-scale distributed data systems.
Relevant certifications:
Cloud (AWS/Azure) Professional
Data engineering or analytics platform certifications
Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.

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