Senior Data Engineer

Ascendion · Remote

Source and collection record

Last observed by our collector: 8 October 2026 at 11:05 pm IST. This is not a manual review or a confirmation from the employer.

Employer requisition: 50342958

Job type and experience labels are automated discovery categories. The employer determines eligibility. Original experience wording was not retained separately; check the current employer page.

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Collected minimum qualification: Experience with Delta Lake and Parquet.Attendance and availability: I checked the employer page for work location, office attendance, start date and duration.Current requisition: I checked the current employer requisition and its application requirements.

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Collected role description

Key Responsibility Areas: Design and develop scalable, secure, and high-performance data engineering solutions on Microsoft Azure. Architect and implement complex ETL/ELT data pipelines using Azure Data Factory and Azure Databricks. Develop highly optimized PySpark applications and Databricks notebooks for large-scale data processing. Design and implement batch and incremental data processing frameworks. Develop complex SQL queries, stored procedures, CTEs, window functions, and data transformations. Design and implement data ingestion solutions from multiple structured and semi-structured data sources. Work extensively with Azure Data Lake Storage Gen2, Delta Lake, and Parquet. Design and implement Medallion Architecture (Bronze, Silver, Gold) and modern data lake/lakehouse solutions. Optimize Databricks clusters, Spark jobs, PySpark code, SQL queries, and data pipelines for performance and cost. Develop robust error handling, logging, monitoring, and data validation mechanisms. Implement data quality, reconciliation, and validation frameworks. Troubleshoot complex production issues and provide root-cause analysis and permanent solutions. Lead technical discussions and contribute to data architecture and solution design. Mentor junior and mid-level data engineers and provide technical guidance. Collaborate with Data Architects, Developers, Data Scientists, Business Analysts, and other stakeholders. Participate in code reviews and establish engineering best practices and coding standards. Implement CI/CD processes for data engineering workloads using Azure DevOps/Git. Ensure solutions comply with organizational standards for security, governance, scalability, and reliability. Mandatory Technical Skills 7–11 years of overall experience in Data Engineering / Data Platforms. Strong hands-on experience with Azure Databricks. Strong experience with Azure Data Factory (ADF). Excellent hands-on experience with PySpark and Python. Advanced proficiency in SQL. Strong understanding of Apache Spark and distributed data processing. Experience with Delta Lake and Parquet. Strong experience designing and developing ETL/ELT pipelines. Strong understanding of data warehousing and data modeling concepts. Experience with Azure Data Lake Storage Gen2 (ADLS Gen2). Experience with performance tuning and optimization of Spark/PySpark workloads. Experience with production support, troubleshooting, monitoring, and data pipeline optimization.

Collected minimum qualification

Experience with Delta Lake and Parquet.

Collected experience requirement

7-11 years