Senior ETL Data Engineer

Kavi India; Chennai District; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

Kavi India is seeking a Senior ETL Data Engineer to design, build, and optimize scalable data pipelines powering analytics, reporting, and ML initiatives across the organization. You will own the full lifecycle from ingestion to transformation to delivery and enable downstream BI with well-structured datasets.

You will mentor junior engineers, drive best practices in data engineering, and partner with BI teams to design semantic layers.

Qualifications

• 8-10 years of hands-on experience in data engineering with a strong focus on ETL/ELT pipeline development.
• Strong proficiency in SQL and at least one programming language (Python preferred).
• Hands-on experience with ETL/orchestration tools 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 with relational databases (PostgreSQL, MySQL, SQL Server) and big data technologies (Spark, Hadoop, Hive).
• Strong understanding of data warehousing concepts, dimensional modeling, and data architecture principles.
• Working knowledge of Power BI — building data models, writing DAX, and designing dashboards/reports connected to enterprise data pipelines.
• Understanding of Power BI performance optimization (incremental refresh, aggregations, query folding, Import vs. DirectQuery).
• Experience with data pipeline orchestration, scheduling, and monitoring frameworks.
• Familiarity with version control (Git) and CI/CD pipelines for data engineering workflows.

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 for efficient BI consumption
• Own end-to-end pipeline orchestration, monitoring, and error handling to ensure high reliability and data quality
• Optimize SQL queries and pipeline performance for large-scale datasets
• Partner with BI developers and business stakeholders to design semantic layers and datasets that support Power BI dashboards and reports
• Build and maintain Power BI data models (star schema), DAX measures, and dataset refresh pipelines, ensuring alignment with underlying ETL structures
• Optimize Power BI datasets and queries for performance, including incremental refresh strategies and efficient data source connections (Import vs. DirectQuery)
• Implement data quality checks, validation frameworks, and observability/monitoring for pipelines
• Manage and evolve CI/CD practices for data pipeline (and where applicable, Power BI deployment pipeline) releases
• Ensure data governance, security, and row-level security (RLS) standards are met across pipelines and Power BI reports
• Mentor junior data engineers and contribute to engineering best practices and documentation
• Troubleshoot and resolve production pipeline and reporting issues, ensuring minimal downtime

Skills

SQL

Python

Data modeling

Power BI knowledge

CI/CD for data

Tools

Apache Airflow

dbt

Informatica

Talend

SSIS

AWS Redshift

Azure Data Factory

GCP BigQuery

Spark

Hadoop

Hive

Job description

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

About the Role

We're looking for a Senior ETL Data Engineer to design, build, and optimize scalable data pipelines that power analytics, reporting, and machine learning initiatives across the organization. You'll own the full lifecycle of data pipeline development — from ingestion to transformation to delivery — while also enabling downstream BI consumption through well-structured, reporting-ready datasets. You'll mentor junior engineers 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 for efficient BI consumption
• Own end-to-end pipeline orchestration, monitoring, and error handling to ensure high reliability and data quality
• Optimize SQL queries and pipeline performance for large-scale datasets
• Partner with BI developers and business stakeholders to design semantic layers and datasets that support Power BI dashboards and reports
• Build and maintain Power BI data models (star schema), DAX measures, and dataset refresh pipelines, ensuring alignment with underlying ETL structures
• Optimize Power BI datasets and queries for performance, including incremental refresh strategies and efficient data source connections (Import vs. DirectQuery)
• Implement data quality checks, validation frameworks, and observability/monitoring for pipelines
• Manage and evolve CI/CD practices for data pipeline (and where applicable, Power BI deployment pipeline) releases
• Ensure data governance, security, and row-level security (RLS) standards are met across pipelines and Power BI reports
• Mentor junior data engineers and contribute to engineering best practices and documentation
• Troubleshoot and resolve production pipeline and reporting issues, ensuring minimal downtime

Required Skills & Qualifications

• 8-10 years of hands‑on experience in data engineering with a strong focus on ETL/ELT pipeline development
• Strong proficiency in SQL and at least one programming language (Python preferred)
• Hands‑on experience with ETL/orchestration tools 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 relational databases (PostgreSQL, MySQL, SQL Server) and big data technologies (Spark, Hadoop, Hive)
• Strong understanding of data warehousing concepts, dimensional modeling, and data architecture principles
• Working knowledge of Power BI — building data models, writing DAX, and designing dashboards/reports connected to enterprise data pipelines
• Understanding of Power BI performance optimization (incremental refresh, aggregations, query folding, Import vs. DirectQuery trade‑offs)
• Experience with data pipeline orchestration, scheduling, and monitoring frameworks
• Familiarity with version control (Git) and CI/CD pipelines for data engineering workflows