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Tier-1 brand, popular mid-level Data Engineer title, and metro location increase competition.
Role requires deep Databricks/Spark and financial data governance expertise, limiting cross-industry transferability.
Strict Databricks, Spark, Python, cloud, and data governance requirements enforce tight technical filters.
Design, develop, and deploy scalable batch and real-time data pipelines using Python, PySpark, Spark SQL, and Databricks.
Lead technical guidance on pipeline architecture, distributed computing, and cloud-native integrations, including cloud deployments on AWS or GCP.
Implement data quality frameworks, optimize Spark and SQL performance, and drive CI/CD automation and rigorous data governance standards.
Minimum 3 years hands-on experience developing within Databricks including Delta Lake, Delta Live Tables, Unity Catalog, and Databricks Workflows.
Strong production-grade proficiency in Python (including pandas and pytest) and advanced SQL with query optimization skills.
Extensive experience deploying Databricks on AWS or GCP with cloud-native components like S3/GCS, IAM, and serverless query engines.
Work Experience Required: Minimum 3 years in relevant data engineering role involving distributed computing and cloud platforms.
Experienced in architecting and optimizing distributed data pipelines and Spark clusters at scale within Unified Lakehouse environments.
Technically strong leader capable of mentoring and enforcing best practices in an Agile/Scrum setup.
Skilled in integrating complex cloud infrastructure with data engineering workflows and collaborating with Data Science and BI teams for operationalizing ML models and reporting.