Job Title: Associate – Site Reliability Engineer (Data Engineering)
Location: Bengaluru, Karnataka, India
Employment Type: Full Time (Permanent)
Experience Required: 1–4 Years
As an Associate Site Reliability Engineer (SRE) within the Data Engineering team at Goldman Sachs, you will work at the intersection of software engineering and platform reliability to ensure robust, scalable, and cost-effective data systems.
You will support some of Goldman Sachs’s largest data platforms, driving observability, operational excellence, and strategic platform enhancements.
You will partner with engineers and analysts across the firm to enable smooth, reliable, and performant access to critical data systems.
Drive cloud adoption strategies for data processing and warehousing solutions.
Lead SRE strategy and implementation for large-scale data platforms including Data Lake and Lakehouse systems.
Engage with data consumers and producers to align platform reliability with business cost and performance expectations.
Design and implement observability, performance, and cost monitoring strategies using modern SRE tools.
Develop and maintain automation for DevOps and CI/CD pipelines in support of platform efficiency and risk management.
Take full lifecycle responsibility for platforms — from design through deprecation — including on-call rotation and postmortem analysis.
Use data-driven approaches to inform platform decisions and continuous improvement efforts.
Contribute to documentation, knowledge sharing, and best practice development across engineering teams.
Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
1–4 years of professional experience in software engineering or SRE roles.
Minimum 1–2 years of hands-on coding experience (Python or Java preferred).
Understanding of SRE and DevOps principles, automation, and operational risk mitigation.
Experience working with cloud infrastructure (AWS, Azure, or GCP).
Proven track record in data strategy, platform reliability, and cost optimization.
Proficiency in SQL and data warehousing concepts including schema design, indexing, and partitioning.
Familiarity with relational and columnar databases and performance tuning.
Strong understanding of data governance including quality, traceability, latency, and security.
Excellent analytical and problem-solving skills with a commercial focus.
Effective communication skills with the ability to translate complex data concepts into actionable strategies.
Self-motivated, collaborative, and capable of working in fast-paced global environments.
Experience with Snowflake or other cloud-based databases such as BigQuery.
Familiarity with Data Lake / Lakehouse architectures, including technologies like Apache Iceberg.
Working knowledge of open-source tools such as AWS Lambda, Prometheus, and OpenTelemetry.
Experience with observability tools like Grafana and PromQL.
Exposure to GitLab CI/CD, infrastructure as code, and microservices-based environments.
Understanding of data modeling concepts and entitlement implementations.
Programming: Python, Java
Platforms: AWS, Snowflake, BigQuery
Tools: Prometheus, Grafana, OpenTelemetry, GitLab CI/CD
Databases: SQL (relational and columnar), NoSQL
SRE Concepts: Monitoring, Reliability, Capacity Planning, Automation
