Lead Cloud & Big Data Architect

Primary Skills : GCP, Bigdata

Seondary Skills : Python/Spark

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. pyspark, GCP, spark, sql

Primary Skills : GCP, Bigdata

Seondary Skills : Python/Spark

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).