Scalable Data Architect

23 hours ago


Pune, Maharashtra, India beBeeData Full time ₹ 18,23,239 - ₹ 24,30,987
Senior Data Engineer - Azure Databricks

We are seeking a seasoned professional to lead the development of scalable data pipelines and streaming architectures using Azure-native technologies.

Main Responsibilities :

  • Design, develop, and optimize data pipelines using Databricks (PySpark) for batch and real-time data processing.
  • Implement CDC (Change Data Capture) and Delta Live Tables/Autoloader to support near-real-time ingestion.
  • Integrate various structured and semi-structured data sources using ADF, ADLS, and Kafka (Confluent).
  • Develop CI/CD pipelines for data engineering workflows using GitHub Actions or Azure DevOps.
  • Write efficient and reusable SQL and Python code for data transformations and validations.
  • Ensure data quality, lineage, governance, and security across all ingestion and transformation layers.
  • Collaborate closely with business analysts, data scientists, and data stewards to support use cases in risk, finance, compliance, and operations.
  • Participate in code reviews, architectural discussions, and documentation efforts.

Required Skills & Qualifications :

  • Strong proficiency in SQL, Python, and PySpark.
  • Proven experience with Azure Databricks, including notebooks, jobs, clusters, and Delta Lake.
  • Experience with Azure Data Lake Storage (ADLS Gen2) and Azure Data Factory (ADF).
  • Hands-on with Confluent Kafka for streaming data integration.
  • Strong understanding of Autoloader, CDC mechanisms, and Delta Lake-based architecture.
  • Experience implementing CI/CD pipelines using GitHub and/or Azure DevOps.
  • Knowledge of data modeling, data warehousing, and data security best practices.
  • Exposure to regulatory and risk data use cases in the banking/financial sector is a strong plus.

Preferred Qualifications :

  • Azure certifications (e.g., Azure Data Engineer Associate).
  • Experience with tools such as Delta Live Tables, Unity Catalog, and Lakehouse architecture.
  • Familiarity with business glossaries, data lineage tools, and data governance frameworks.
  • Understanding of financial data including GL, loan, customer, transaction, or market risk domains.


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