R&D Site Reliability Engineer - Data Platform

Position Overview:

We are seeking an exceptional Senior Site Reliability Engineer to drive reliability excellence within Sanofi''s R&D Data Platform team. This role serves as a critical bridge between Digital Technology and R&D Data Product teams, ensuring the reliability, scalability, and efficiency of data platforms that power scientific innovation and drug discovery.

Strategic Importance:

This role directly enables Sanofi''s data-driven drug discovery mission by establishing the reliability foundation for R&D data platforms. You''ll accelerate scientific innovation by optimizing data platform reliability, reducing data pipeline latency, and ensuring cost-effective cloud operations.

Role Description:

1. Data Platform Reliability & Operations

* Design and implement SLOs for data pipelines, data lakes, and analytics platforms

* Establish reliability metrics for data freshness, quality, and availability

* Implement error budget policies that balance data platform stability with research agility

* Monitor and optimize performance of data processing workloads

2. Cloud Infrastructure & FinOps

* Lead AWS infrastructure automation and optimization

* Implement FinOps practices for R&D cloud spend management

* Design cost-effective architectures for data workloads

* Optimize resource utilization for research computing environments

3. Integration Platform Reliability

* Ensure reliability of data integration platforms (Amazon S3, PostGreSQL, DynamoDB and SnowFlake)

* Monitor and optimize ETL/ELT pipeline performance

* Implement automated recovery procedures for integration failures

* Design scalable integration architectures

4. Cloud Observability & Monitoring

* Implement comprehensive observability using Datadog, Prometheus, and Grafana

* Design monitoring strategies for data workloads and pipelines

* Create dashboards for data platform KPIs and SLOs

* Enable end-to-end visibility of data flows

5. DevOps Automation

* Implement Infrastructure as Code using Terraform for cloud provisioning

* Establish GitHub Actions to implement code pipelines

* Automate deployment of data platform components

* Create self-service capabilities for research teams

6. Data Quality & Pipeline Reliability

* Implement data quality monitoring and alerting

* Design automated data validation checks

* Establish data pipeline reliability metrics

* Create recovery procedures for data pipeline failures

Key Performance Indicators:

Platform Reliability (40%)

* Data pipeline SLO compliance rate: 99.5%+

* Data freshness SLA achievement

* Integration platform uptime

* Mean time to recovery for data incidents

Engineering Efficiency (30%)

* Automation coverage for cloud provisioning

* Self-service capability adoption

* Infrastructure as Code implementation

* Reduction in manual operations

Cost Optimization (20%)

* FinOps savings achieved

* Resource utilization optimization

* Cloud cost reduction

* Workload efficiency improvements

Stakeholder Success (10%)

* Research team satisfaction scores

* Data platform user feedback

* Cross-team collaboration effectiveness

* Knowledge sharing impact

Required Experience:

Must Have:

* 8+ years of experience in Site Reliability Engineering or similar roles

* Expert-level AWS cloud infrastructure experience

* Strong background in data platforms (Snowflake, Amazon S3, Amazon RDBMS)

* Experience with observability platforms (Datadog)

* Proficiency in Infrastructure as Code (Terraform) and GitHub Actions for CI/CD

* Understanding of data integration platforms and ETL/ELT processes

Preferred:

* Experience supporting scientific or research computing environments

* Knowledge of FinOps practices and cloud cost optimization

* Familiarity with pharmaceutical R&D processes

* Experience with streaming platforms (Kafka, Kinesis)

* Background in data quality and governance

Cultural Attributes:

* Passion for enabling scientific innovation through technology

* Strong collaboration skills with research teams

* Balance between reliability and research agility

* Data-driven decision making

* Continuous improvement mindset

* Ability to translate technical concepts for research audiences

Technologies:

* **Cloud Platforms:** AWS (primary), S3, Glue

* **Data Platforms:** Snowflake, Databricks, PostGreSQL, DynamoDB

* **Integration:** Informatica, Amazon API Gateway

* **Observability:** Datadog

* **Automation:** Terraform, GitHub Actions

* **Containers:** EKS, Kubernetes

* **Streaming:** Kafka, Kinesis

* **Orchestration:** Airflow, Step Functions

Success Timeline

First 30 Days

* Understand R&D data platform architecture and dependencies

* Assess current reliability practices and monitoring coverage

* Identify immediate optimization opportunities

* Build relationships with R&D and Digital Technology teams

60-90 Days

* Implement initial observability improvements

* Establish baseline SLOs for critical data pipelines

* Begin FinOps analysis and optimization

* Create automation roadmap

90-180 Days

* Deploy comprehensive monitoring solution

* Implement key automation workflows

* Establish FinOps practices

* Deliver first wave of self-service capabilities

6+ Months

* Lead strategic reliability initiatives

* Drive continuous platform optimization

* Establish best practices and standards

* Enable accelerated research capabilities through reliable data platforms