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Senior level and niche Databricks/GCP specialization reduces qualified applicant density.
Role requires specialized Databricks and GCP data engineering experience, limiting cross-industry transferability.
Explicit 10–16 years requirement plus mandatory Databricks, PySpark and GCP skills create strict filters.
Own the end-to-end design, architecture, development, testing, and maintenance of scalable ETL/ELT data pipelines using Python, PySpark, Databricks, and GCP/Azure.
Directly engage with customers to gather requirements, perform solution discovery and architect enterprise data solutions leveraging GCP services including Dataflow, BigQuery, Cloud Composer, and others.
Ensure data quality, consistency, pipeline optimization, and post-delivery support while collaborating with cross-functional teams including data scientists and analysts.
10 to 16 years of relevant IT experience in backend or data engineering roles.
Strong hands-on experience with Python, PySpark, Databricks notebooks and workflows, and Spark SQL.
Proven expertise working with Google Cloud Platform services such as Dataflow, BigQuery, Cloud Functions, Cloud Composer, IAM, Cloud Run.
Bachelor's degree in Computer Science or related field or equivalent experience.
Experienced in architecting data solutions using Medallion Architecture (Bronze, Silver, Gold layers) and Delta Lake implementation on Databricks.
Skilled in tuning Spark jobs, optimizing cluster configurations, and managing large-scale data processing performance.
Familiar with cross-cloud tools, including Azure Data Factory, Azure Databricks, and CI/CD pipelines using GitHub, showcasing a versatile cloud data engineering background.