Data Reliability Engineer

PanAgora Asset Management is focused on transforming financial lives through a flexible work environment and internal mobility. They are seeking a hands-on Data Reliability Engineer to ensure the reliability and operational excellence of their AWS-based data platform, while collaborating with engineering teams to improve data systems and resolve production issues.

Responsibilities:

• Own the reliability and stability of production data pipelines and data platform services
• Diagnose and resolve data pipeline failures, delays, and data quality issues in production environments
• Investigate issues across distributed data systems (e.g., Spark/EMR workloads, ingestion pipelines, warehouse performance)
• Lead or support incident response, including triage, mitigation, and long-term resolution
• Perform root cause analysis (RCA) and implement durable fixes to prevent recurrence
• Define and improve data SLAs (freshness, latency, completeness) and ensure adherence
• Design and enhance monitoring, alerting, and observability for data systems
• Develop automation and tooling to reduce operational toil and improve system resilience
• Contribute to disaster recovery (DR) and resiliency planning, including backup validation and recovery workflows
• Partner with engineering teams to improve pipeline design, reliability, and operational readiness
• Create and maintain runbooks, SOPs, and operational documentation
• Participate in occasional off-hours support for production data systems when required

Requirements:

• Strong experience working with production data platforms in AWS environments
• Prior experience building data pipelines and seeing them through production, including exposure to real-world failures and operational challenges
• Strong experience with Python and SQL in real data systems
• Hands-on experience troubleshooting distributed data processing systems (e.g., Spark/EMR, Redshift, streaming systems)
• Proven ability to debug and resolve production issues in data pipelines and data platforms
• Experience with AWS data services (such as EMR, Redshift, DynamoDB, S3, or similar)
• Experience handling production incidents and performing root cause analysis
• Strong problem-solving mindset and ability to work through ambiguous production issues
• Experience handling real-world data issues such as pipeline delays or failures
• Experience with backfills and reprocessing
• Experience with late-arriving or incomplete data
• Experience improving observability and alerting specifically for data systems
• Experience influencing or guiding data pipeline reliability and operational practices
• Exposure to streaming/event-driven systems (Kafka, Kinesis, CDC patterns)
• Experience with disaster recovery, backup validation, and resiliency testing
• Strong communication during incidents with both technical and non-technical stakeholders