Support and troubleshoot production GCP data pipelines across batch and streaming workloads. Diagnose failures, latency, performance, and configuration issues; optimize pipelines for reliability and faster refreshes; support platform upgrades and compliance; and collaborate with Data Engineering, BI, and application teams. The role requires hands-on GCP, SQL, Python, BigQuery, Spanner, Dataflow, Pub/Sub, IAM, monitoring, logging, and production support experience.
This is a remote position.
Support production of GCP data pipelines and workflows across batch and streaming workloads.
Troubleshoot failures, data latency, performance and configuration issues.
Work with Cloud Spanner, BigQuery, Dataflow, Pub/Sub and related GCP services.
Optimize existing pipelines to improve data refresh times and reliability.
Support platform/configuration upgrades and ensure environments remain compliant.
Work with Data Engineering, BI and application teams on production issues and enhancements.
Quickly understand existing systems and make practical improvements.
Requirements
3–7 years of Data/Cloud Engineering experience with hands-on GCP.
Strong SQL and Python skills.
Experience with Spanner, BigQuery and batch/streaming pipelines.
Good understanding of GCP IAM, monitoring, logging and production support.
Strong troubleshooting mindset and ability to ramp up quickly in a complex environment.
Exposure to Tableau/Looker, App Engine or AI/LLM applications is a plus.
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Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
