Job Title - Digital R&D Integration Manager
Location - Hyderabad
Department / Organization
Organization: Digital R&D — Software Engineering Sub-Domain: Data Integration Engineering Business Context: Sanofi's Digital R&D organization is at the forefront of transforming drug discovery, clinical development, and translational science through data-driven innovation. This role sits within the Software Engineering function and is critical to enabling seamless data flows across R&D platforms, scientific systems, and enterprise data ecosystems.
Position Summary
The Digital R&D Integration Manager is a senior people manager and technical lead responsible for designing, building, and operating robust ETL (Extract, Transform, Load) and reverse ETL data integration pipelines that power Sanofi's R&D data ecosystem. This role bridges scientific data sources — including laboratory systems, clinical platforms, genomics databases, and regulatory repositories — with enterprise data platforms, analytics environments, and operational applications.
The Integration Lead will directly manage a team of integration engineers, combining people leadership with hands-on engineering excellence. In addition to guiding team members' growth, performance, and day-to-day delivery, the Lead will remain deeply involved in architecture decisions, pipeline development, and platform optimization. The ideal candidate brings deep expertise in data integration patterns — including Informatica Intelligent Cloud Services (IICS) — cloud-native data platforms, and an understanding of the unique data governance and compliance requirements of the pharmaceutical industry.
Key Responsibilities
People Management & Team Leadership
- Directly manage a team of integration engineers, serving as their primary people manager for performance, development, and day-to-day guidance
- Conduct regular 1:1s, performance reviews, and career development conversations with direct reports
- Set clear goals, expectations, and success metrics for team members aligned with organizational priorities
- Recruit, onboard, and retain top integration engineering talent; actively participate in hiring processes including interviews and candidate evaluation
- Identify skill gaps and training needs within the team and develop targeted upskilling plans
- Foster a high-performance, inclusive team culture built on psychological safety, accountability, and continuous improvement
- Manage team capacity, workload distribution, and prioritization across concurrent integration projects
- Serve as an escalation point for technical and delivery challenges faced by team members
- Provide mentorship and coaching to junior and mid-level engineers to accelerate their professional growth
- Represent the integration engineering team in cross-functional leadership forums and planning sessions
ETL & Reverse ETL Engineering
- Design, develop, and maintain end-to-end ETL pipelines ingesting data from diverse R&D sources including LIMS, ELN, clinical trial management systems (CTMS), genomics platforms, and external scientific databases
- Architect and implement reverse ETL workflows to operationalize curated data from the enterprise data warehouse/lakehouse back into operational R&D tools (e.g., Veeva, Spotfire, Benchling, Salesforce)
- Lead the design and implementation of integration solutions using Informatica Intelligent Cloud Services (IICS), including IICS Data Integration, Cloud Mass Ingestion, and Application Integration services
- Build robust data transformation logic to harmonize heterogeneous scientific data formats (HL7 FHIR, CDISC SDTM/ADaM, JSON, Parquet, XML, CSV) into standardized, analytics-ready structures
- Implement incremental, CDC (Change Data Capture), and event-driven ingestion patterns to ensure data freshness and pipeline efficiency
- Develop and maintain data quality checks, validation rules, and reconciliation frameworks within pipelines
Data Integration Architecture
- Lead the design of scalable, reusable integration patterns and frameworks across the R&D data landscape
- Define and enforce integration standards, naming conventions, and metadata management practices
- Evaluate and recommend integration tools, platforms, and architectural approaches — including IICS capabilities — aligned with Sanofi's technology strategy
- Collaborate with data architects to ensure pipelines align with the enterprise data mesh / lakehouse architecture
- Design for fault tolerance, idempotency, retry logic, and observability across all integration workflows
Pipeline Management & Operations
- Own the operational health of all integration pipelines — monitoring, alerting, SLA management, and incident response
- Implement pipeline observability using logging, lineage tracking, and data quality dashboards
- Manage pipeline versioning, CI/CD deployment, and environment promotion (dev staging production)
- Conduct root cause analysis for pipeline failures and implement preventive measures
- Optimize pipeline performance, cost efficiency, and scalability on cloud platforms
R&D Domain Integration
- Partner with R&D data scientists, bioinformaticians, and clinical data managers to understand data requirements and translate them into integration solutions
- Integrate data from clinical trials, preclinical studies, omics platforms, and regulatory submissions
- Support data provisioning for AI/ML model training pipelines and analytical workbenches
- Enable real-time and near-real-time data flows for operational R&D use cases
Compliance & Data Governance
- Ensure all pipelines comply with GxP, GDPR, HIPAA, and 21 CFR Part 11 regulatory requirements
- Implement data lineage, audit trails, and access controls within integration workflows
- Collaborate with Data Governance and Compliance teams to enforce data classification and handling policies
- Support data validation documentation required for regulated system integrations
Required Qualifications
Education
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Bioinformatics, or a related technical field
- Master's degree preferred
Experience
- 12+ years of overall experience in data engineering, software engineering, or integration engineering
- 5+ years of hands-on experience designing and building ETL/ELT pipelines in production environments
- 3+ years of direct people management experience, including performance management, career development, and team building
- 2+ years of hands-on experience with Informatica Intelligent Cloud Services (IICS) in a production environment
- 2+ years of experience with reverse ETL patterns and tools (e.g., Census, Hightouch, or custom implementations)
- 2+ years of experience managing integration engineering teams, including hiring, onboarding, and developing engineers
- Demonstrated ability to balance people leadership responsibilities with hands-on technical contributions
- Experience working in pharmaceutical, biotech, healthcare, or life sciences environments strongly preferred
- Demonstrated experience with regulated data environments (GxP, HIPAA, GDPR)
- Experience with data mesh architecture and domain-oriented data ownership models
- Contributions to open-source data engineering projects
- Relevant certifications: AWS/Azure/GCP Data Engineer, Databricks Certified, dbt Analytics Engineer, Informatica IICS Certification
- Experience with MLOps pipelines and feature store integrations
- Prior experience building and scaling integration engineering teams from the ground up
Technical Skills Required
ETL / ELT & Integration Tools
Category
Technologies
ETL/ELT Orchestration
Informatica IICS (required), Apache Airflow, Prefect, Dagster, Azure Data Factory, AWS Glue
IICS Services
IICS Data Integration, Cloud Mass Ingestion, Application Integration, Data Quality, API Center
Reverse ETL
Census, Hightouch, custom Python-based reverse ETL frameworks
Streaming / Event-Driven
Apache Kafka, AWS Kinesis, Azure Event Hubs, Spark Streaming
Transformation
dbt (data build tool), Apache Spark, PySpark, SQL
API Integration
REST APIs, GraphQL, SOAP, Webhooks, OpenAPI
CDC Tools
Debezium, Fivetran, Airbyte, Stitch
Cloud & Data Platforms
Category
Technologies
Cloud Platforms
AWS, Microsoft Azure, Google Cloud Platform
Data Lakehouse
Databricks, Delta Lake, Apache Iceberg, Apache Hudi
Data Warehouses
Snowflake, Azure Synapse, BigQuery, Amazon Redshift
Object Storage
AWS S3, Azure Data Lake Storage (ADLS), GCS
Programming & Scripting
- Python (primary) — pandas, PySpark, SQLAlchemy, FastAPI
- SQL — advanced query optimization, window functions, stored procedures
- Scala (preferred for Spark workloads)
- Shell scripting / Bash
DevOps & Engineering Practices
- CI/CD: GitHub Actions, Azure DevOps, Jenkins
- Containerization: Docker, Kubernetes
- Infrastructure as Code: Terraform, CloudFormation
- Version Control: Git, GitHub/GitLab
- Monitoring & Observability: Great Expectations, Monte Carlo, DataDog, Grafana
Data Governance & Security
- Data Cataloging: Collibra, Alation, Apache Atlas
- Data Lineage: OpenLineage, Marquez
- Access Control: IAM, RBAC, column-level security in Snowflake/Databricks
- Audit & Compliance: 21 CFR Part 11 audit trail implementation
Leadership and Soft Skills
- People Leadership: Proven ability to manage, develop, and inspire a team of engineers; comfortable with performance management, conflict resolution, and talent development
- Technical Vision: Ability to define and communicate a clear integration architecture roadmap aligned with business and R&D objectives
- Stakeholder Management: Skilled at translating complex technical concepts for non-technical R&D stakeholders (scientists, clinical operations, regulatory affairs)
- Problem Solving: Strong analytical mindset with the ability to diagnose complex data pipeline issues under pressure
- Collaboration: Proven ability to work across global, cross-functional teams spanning data science, IT, compliance, and business
- Coaching & Mentorship: Passion for growing engineering talent and fostering a culture of technical excellence; experience running structured mentorship programs
- Agile Mindset: Experience working in Agile/Scrum delivery frameworks with iterative development cycles
- Communication: Excellent written and verbal communication skills; ability to produce clear technical documentation and communicate effectively at all organizational levels
- Ownership: Strong sense of accountability for pipeline reliability, data quality, delivery commitments, and team outcomes
- Adaptability: Comfortable navigating ambiguity in a fast-paced, innovation-driven R&D environment
- Talent Development: Track record of identifying high-potential engineers and creating growth opportunities that retain top talent
What We Offer
At Sanofi, we are committed to chasing the miracles of science — and we know that starts with empowering our people.
- Global Impact: Contribute to R&D digital capabilities that directly accelerate drug discovery and improve patient outcomes worldwide
- Innovation at Scale: Work with cutting-edge data platforms, AI/ML ecosystems, and cloud-native architectures including IICS
- Learning & Development: Access to continuous learning programs, certifications, and Sanofi's internal digital academy
- Inclusive Culture: Be part of a diverse, global team that leads together and values every perspective
- Competitive Compensation: Attractive salary, performance-based bonus, and comprehensive benefits package
- Flexible Working: Hybrid work model supporting work-life balance
- Career Growth: Clear career Progression pathways within Sanofi's Digital organization , including people leadership tracks
- Health & Wellbeing: Comprehensive health coverage, wellness programs, and employee assistance resources
10. How to Apply
Interested candidates are encouraged to apply through Sanofi's official careers portal at careers.sanofi.com.
Please include:
- An updated resume/CV highlighting relevant ETL/data integration experience and people management history
- A brief cover letter describing your experience with data pipeline architecture, IICS, team management, and any life sciences industry exposure
For internal candidates, please discuss your interest with your current manager and apply via Workday using the internal job posting reference.
