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Tier-1 brand, metro location, and a mid-level Data Engineer role with common Databricks/Spark skills increases competition.
Core data engineering skills transfer well across industries, though financial domain knowledge moderately matters.
Mandatory Databricks/Spark and cloud data stack requirements create high technical filtering despite no explicit years.
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Design, develop, and optimize Databricks-based scalable ETL data pipelines within a cloud architecture supporting financial analytics systems.
Manage and troubleshoot Databricks jobs, clusters, workflows ensuring production stability and performance efficiency.
Collaborate with cross-functional teams in Agile environment to deliver high-performance, cost-efficient data processing frameworks integrating Snowflake and Azure.
Strong experience with Databricks and Apache Spark SQL / PySpark / Scala for ETL pipeline development and optimization.
Knowledge of Snowflake data warehousing concepts and Azure cloud platform.
Proven ability in troubleshooting complex data systems and optimizing Databricks workloads for reliability and cost.
Work Experience Required: Not explicitly mentioned in the JD.
Deep expertise in distributed data processing frameworks and cloud-native data engineering (Databricks, Azure).
Experienced working in production-grade, scalable data ecosystems supporting financial analytics or reference data platforms.
Comfortable operating in Agile teams with cross-functional collaboration focused on high-performance data solutions.