Reliability Engineering L2-2

Location: Prakāshamnagar

About The Job

We are seeking an exceptional 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

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
Cloud Infrastructure & Fin Ops
Lead AWS infrastructure automation and optimization
Implement Fin Ops practices for R&D cloud spend management
Design cost-effective architectures for data workloads
Optimize resource utilization for research computing environments
Integration Platform Reliability
Ensure reliability of data integration platforms (Amazon S3, PostGreSQL, Dynamo

DB and Snow Flake)
Monitor and optimize ETL/ELT pipeline performance
Implement automated recovery procedures for integration failures
Design scalable integration architectures
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
Dev Ops Automation
Implement Infrastructure as Code using Terraform for cloud provisioning
Establish Git Hub Actions to implement code pipelines
Automate deployment of data platform components
Create self-service capabilities for research teams
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%)

Fin Ops 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 Git Hub Actions for CI/CD
Understanding of data integration platforms and ETL/ELT processes

Preferred

Experience supporting scientific or research computing environments
Knowledge of Fin Ops 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, Git Hub 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 Fin Ops analysis and optimization
Create automation roadmap

90-180 Days

Deploy comprehensive monitoring solution
Implement key automation workflows
Establish Fin Ops 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…