Data Engineer

2 weeks ago

Delhi NCR New Delhi, India Thoucentric Full time US$ 90,000 - US$ 1,20,000 per year

  • Design, develop, and maintain robust data pipelines using Azure Data Factory (ADF).- Build ETL/ELT processes that ingest, transform, and load data from various sources into data lakes, warehouses, or databases. - Implement both batch and near real-time data flows.

  • Create, schedule, and monitor ADF pipelines. - Implement complex data workflows, including dependency management, parameterization, and dynamic pipeline design. - Integrate ADF with other Azure services (Blob Storage, Synapse, Databricks, Key Vault, etc.). - Implement logging, alerting, and retry logic for production stability.

  • Analyze large datasets to identify trends, issues, and transformation needs. - Write performant SQL queries and transformations. - Collaborate with data analysts to understand data requirements and structure transformations accordingly. - Ensure data is accurate, consistent, and aligned with business logic.

  • Quickly diagnose and resolve data quality issues, pipeline failures, or performance bottlenecks. - Perform root cause analysis for pipeline errors and implement long-term fixes. - Debug and optimize Spark or SQL transformations in environments like Databricks.

  • Implement data validation and quality checks within pipelines. - Perform reconciliation and sanity checks across data sources. - Maintain data dictionaries and metadata repositories.

  • Work closely with data architects, analysts, BI developers, and business users. - Translate business data needs into technical implementations. - Document pipeline logic, data flows, and troubleshooting procedures.

  • Work closely with data architects, analysts, BI developers, and business users. - Translate business data needs into technical implementations. - Document pipeline logic, data flows, and troubleshooting procedures.

  • Optimize ADF pipeline execution time and cost. - Improve query performance using indexing, partitioning, and efficient transformation logic. - Recommend improvements in data infrastructure and tooling.

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