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Tier-1 brand, mid-level generalist Data Engineer, metro location, and broad tech requirements increase applicant competition.
Core data engineering skills are transferable, but Scala/Spark and cloud specifics create moderate industry bias.
Explicit 4–6 years plus mandatory Scala, Spark, GCP and datastore skills make filters stringent.
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Design, build, and optimize scalable, reliable data pipelines primarily using Scala, SQL, and Apache Spark for analytics and data consumption.
Develop and maintain ETL/ELT processes on Google Cloud Platform (GCP) and support workloads on Oracle Cloud Infrastructure (OCI).
Collaborate with analytics, data science, and product teams to ensure data quality, performance, and availability at scale.
4 to 6 years of professional data engineering experience with hands-on big data processing.
Proficiency in Scala programming, advanced SQL, and Apache Spark.
Experience with GCP data services such as BigQuery, Cloud Composer, and Cloud Storage; multi-cloud exposure including OCI is a plus.
Work Experience Required: 4 to 6 years in data engineering and big data ecosystems.
Strong expertise in distributed data processing frameworks and performance optimization for large-scale datasets.
Experience designing robust, scalable, and maintainable data architectures in cloud environments, especially GCP and OCI.
Comfortable collaborating with cross-functional teams including analytics, data science, and product, delivering high-quality data solutions.