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Tier-1 brand, mid-level data engineer, metro location and broad big-data skillset increases competition.
Data engineering skills transfer across industries, though Scala/Spark/GCP expertise favors big-data environments.
Explicit 4–6 years plus mandatory Scala, Spark and cloud data stack creates strict filters.
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Design, build, and maintain scalable, reliable data pipelines enabling high-quality analytics across the organization.
Develop and optimize ETL/ELT processes using Scala, SQL, Apache Spark on cloud platforms, primarily Google Cloud Platform (GCP) with some Oracle Cloud Infrastructure (OCI) support.
Collaborate with analytics, data science, and product teams to ensure data reliability, performance, and availability at scale.
4 to 6 years of hands-on data engineering experience with big data processing.
Proficient in Scala programming and advanced SQL for complex data transformations.
Experience with Apache Spark and big data technologies such as Cassandra and Redis.
Experience working with Google Cloud Platform services (BigQuery, Cloud Composer, Cloud Storage) and familiarity with multi-cloud environments including OCI.
Experienced in designing robust, scalable, and maintainable data architectures for large-scale distributed systems.
Skilled in performance optimization for distributed data processing frameworks.
Comfortable collaborating across analytics, data science, and product teams to deliver clean, structured data at enterprise scale.