Impetus - Data Architect - Google Cloud Platform

Description :

Location : Chennai.

Experience : 10-15 years.

Job Description :

- Architect and implement enterprise-grade data migration solutions using Java and Python, enabling seamless data transfers from on-premises to GCP (Cloud Storage, BigQuery, Pub/Sub) using Apache Airflow and Google Cloud Composer.

- Build secure, scalable, and optimized data architectures leveraging GCP services such as Cloud Storage, Pub/Sub, Dataproc, Dataflow, and BigQuery.

- Design and implement automated frameworks for data delivery, monitoring, and troubleshooting.

- Develop data observability frameworks to ensure quality, lineage, and reliability across pipelines.

- Proactively monitor system performance, identify bottlenecks, and optimize pipelines for efficiency, scalability, and cost.

- Troubleshoot and resolve complex technical issues in distributed systems and cloud environments.

- Drive best practices in documentation of tools, architecture, processes, and solutions.

- Mentor junior engineers, conduct design/code reviews, and influence engineering standards.

- Collaborate with cross-functional teams to enable AI/ML and GenAI-driven use cases on LUMI.

Minimum Qualifications :

- 9+ years of experience in data engineering, software engineering, or platform development.

- Strong programming expertise in Java, Python, and Shell scripting.

- Advanced knowledge of SQL, data modeling, and performance optimization.

- Deep expertise in Google Cloud Platform services: Cloud Storage, BigQuery, Pub/Sub, Dataproc, Dataflow.

- Strong background in RDBMS (Oracle, Postgres, MySQL) and exposure to NoSQL DBs (Cassandra, MongoDB, or similar).

- Proven track record in CI/CD pipelines, Git workflows, and Agile development.

- Demonstrated experience in building and scaling production-grade data pipelines.

- Strong problem-solving and troubleshooting skills in distributed and cloud-native systems.

Preferred Qualifications :

- Hands-on experience with DevOps best practices, automation, and infrastructure as code.

- Exposure to platform engineering (networking, security, IAM, firewalls).

- Experience designing and implementing data observability frameworks (monitoring, lineage, anomaly detection).

- Hands-on or exposure to GenAI integrations (LLMs, RAG, AI-driven data engineering workflows).

- Proven ability to mentor, influence, and lead engineering discussions.