Digital R&D Integration Manager, Raniganj

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 leadershipwith 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 integrationpatterns — 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 prioritiesRecruit, 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 improvementManage 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 growthRepresent 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 structuresImplement 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 architectureDesign 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 workbenchesEnable 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 policiesSupport 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 engineering5+ 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 environment2+ 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 contributionsExperience 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 projectsRelevant 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 RequiredETL / 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 ETLCensus, 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 ToolsDebezium, 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), GCSProgramming&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 …