Data Engineer
Summary
Thoucentric is hiring a Data Engineer (3-5 years' experience, New Delhi) for a consulting role: designing and maintaining Azure Data Factory pipelines, building ETL/ELT flows into data lakes/warehouses, and optimizing SQL and Spark transformations in Databricks, with monitoring, data-quality checks, and performance tuning.
Data Engineer
Location-New Delhi
Exp-3-5 years
- 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.