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Senior ETL Data Engineer

Kavi Software Technologies Private Limited
Posted 8 days ago, valid for 12 days
Location

Thiruporur, TN

Salary

Competitive

Contract type

Full Time

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Sonic Summary

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  • The Senior ETL Data Engineer role requires 8 to 10 years of hands-on experience in data engineering, focusing on ETL/ELT pipeline development.
  • The successful candidate will design, build, and optimize scalable data pipelines for analytics and reporting, while mentoring junior engineers in best practices.
  • Key responsibilities include managing end-to-end pipeline orchestration, optimizing SQL queries, and ensuring data quality and governance standards are met.
  • Strong proficiency in SQL, experience with ETL tools, and knowledge of cloud data platforms are essential for this position.
  • The salary for this role is competitive and commensurate with experience.

Senior ETL Data Engineer
Experience:
8 - 10 years | Level: Senior

About the Role
 We're looking for a Senior ETL Data Engineerto design, build, and optimize scalable data pipelines that power analytics,reporting, and machine learning initiatives across the organization. You'll ownthe full lifecycle of data pipeline development — from ingestion totransformation to delivery — while also enabling downstream BI consumptionthrough well-structured, reporting-ready datasets. You'll mentor juniorengineers and drive best practices in data engineering.

Key Responsibilities

      Design, develop, and maintain robust,scalable ETL/ELT pipelines to ingest data from diverse sources(databases, APIs, flat files, streaming sources)

      Build and optimize data models(star/snowflake schemas) for data warehouses and data lakes, structured forefficient BI consumption

      Own end-to-end pipeline orchestration,monitoring, and error handling to ensure high reliability and data quality

      Optimize SQL queries and pipelineperformance for large-scale datasets

      Partner with BI developers and businessstakeholders to design semantic layers and datasets that support Power BIdashboards and reports

      Build and maintain Power BI data models(star schema), DAX measures, and dataset refresh pipelines, ensuringalignment with underlying ETL structures

      Optimize Power BI datasets and queries for performance, including incremental refresh strategies and efficient datasource connections (Import vs. DirectQuery)

      Implement data quality checks, validationframeworks, and observability/monitoring for pipelines

      Manage and evolve CI/CD practices for datapipeline (and where applicable, Power BI deployment pipeline) releases

      Ensure data governance, security, androw-level security (RLS) standards are met across pipelines and Power BIreports

      Mentor junior data engineers and contributeto engineering best practices and documentation

      Troubleshoot and resolve production pipelineand reporting issues, ensuring minimal downtime

 

Required Skills & Qualifications

      8-10 years of hands-on experience in dataengineering with a strong focus on ETL/ELT pipeline development

      Strong proficiency in SQL and at least oneprogramming language (Python preferred)

      Hands-on experience with ETL/orchestrationtools such as Apache Airflow, dbt, Informatica, Talend, or SSIS

      Solid experience with cloud data platforms(AWS Redshift/Glue, Azure Data Factory/Synapse, or GCP BigQuery/Dataflow)

      Experience working with both relationaldatabases (PostgreSQL, MySQL, SQL Server) and big data technologies (Spark,Hadoop, Hive)

      Strong understanding of data warehousingconcepts, dimensional modeling, and data architecture principles

      Working knowledge of Power BI —building data models, writing DAX, and designing dashboards/reports connectedto enterprise data pipelines

      Understanding of Power BI performanceoptimization (incremental refresh, aggregations, queryfolding, Import vs. DirectQuery trade-offs)

      Experience with data pipeline orchestration,scheduling, and monitoring frameworks

      Familiarity with version control (Git) andCI/CD pipelines for data engineering workflows

 

 

 






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