Role Overview :

We are seeking a seasoned GCP Data Engineer to join our high-performing data engineering team in Bangalore. In this role, you will architect, build, and maintain scalable data pipelines that power our core analytical platforms and machine learning initiatives.

You will work closely with cross-functional teams, including data scientists, product managers, and business stakeholders, to transform complex raw data into actionable insights. Your work will directly influence business decision-making and product innovation, ensuring that our data infrastructure remains robust, performant, and capable of handling massive throughput in a cloud-native environment.

Key Responsibilities :

- Design and implement end-to-end data ingestion and transformation pipelines using Google Cloud Platform services to ensure high data availability for downstream analytics.

- Optimize complex Spark and PySpark jobs to enhance processing efficiency and reduce latency for large-scale data workloads.

- Manage real-time data streaming architectures using Kafka to provide stakeholders with near-instantaneous visibility into business performance.

- Architect high-performance data warehouses in BigQuery, ensuring cost-effective storage and rapid query performance for business intelligence teams.

- Collaborate with engineering leads to migrate legacy on-premise systems to GCP, modernizing our infrastructure to support long-term scalability.

Required Skillset :

- Demonstrated expertise in building distributed data systems using Apache Spark, Scala, and PySpark to solve complex data processing challenges.

- Proven ability to leverage GCP-native tools such as Dataflow and Dataproc to automate and orchestrate data workflows in a production environment.

- Advanced proficiency in SQL for complex data modeling, performance tuning, and analytical querying.

- Strong communication skills with the ability to translate technical data requirements into clear, actionable strategies for non-technical stakeholders.

- Exceptional problem-solving capabilities with a track record of managing large-scale data projects within a fast-paced, hybrid work environment.

- A degree in Computer Science, Engineering, or a related quantitative field, complemented by 5 to 14 years of professional experience in data engineering.