Data Engineer

Data Engineering; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Data Engineering in Bengaluru seeks an experienced Data Engineer to design and execute end-to-end ELT pipelines migrating data from SQL sources into Snowflake and to orchestrate bulk file transfers into Azure Data Lake Storage (ADLS).

You will implement scalable pipelines, optimize SQL workloads, and ensure data quality with reconciliation checks while collaborating with client teams on security and governance standards.

Qualifications

• Experience building production ELT/ETL pipelines and data workflows.
• Strong SQL and performance tuning for large datasets across relational engines.
• Hands-on Snowflake data loading and modeling using staging and bulk loads.
• Solid experience with Azure Data Lake Storage Gen2 and file transfer patterns.
• Familiarity with bulk file ingestion formats (CSV, Parquet, JSON).
• Ability to deliver rapid results within structured project timelines.

Responsibilities

• Design, build, and maintain scalable ELT/ETL pipelines and data workflows for ingestion and transformation.
• Move structured data from SQL sources into Snowflake staging and core layers.
• Oversee file movement tasks into ADLS for bulk transfers.
• Write and optimize high-performance SQL queries for data modeling and validation.
• Perform data reconciliation and completeness checks to prevent data loss.
• Collaborate with engineers to align with platform connectivity, security, and governance.

Skills

ELT pipelines

SQL

Snowflake

Azure Data Lake Storage

File processing

Agile execution

Tools

Airflow

dbt

Azure Data Factory

Python

Git

Job description

Description

Primary Focus: SQL-to-Snowflake Data Transfer, ELT Pipelines & File-to-ADLS Ingestion

Role Overview

We are seeking an experienced Data Engineer to support a high-priority data integration and cloud enablement initiative. The primary focus of this role is designing and executing end-to-end ELT pipelines and data transfer workflows—migrating structured data from SQL sources into Snowflake and orchestrating bulk file transfers into Azure Data Lake Storage (ADLS).

Responsibilities

• Design, build, and maintain scalable ELT/ETL pipelines and data workflows for ingestion and transformation.

• Execute structured data extraction, movement, and landing from relational SQL databases directly into Snowflake staging and core layers.

• Build, execute, and monitor file movement tasks to efficiently transfer flat files, logs, or unstructured formats into Azure Data Lake Storage (ADLS).

• Write and tune high-performance SQL queries for data modeling, validation, data verification, and staging transformations.

• Conduct data reconciliation and completeness checks to ensure zero loss across data pipelines during bulk migration.

• Collaborate closely with the Lead Data Integration Expert and client engineering teams to align with platform connectivity, security, and governance protocols.

Required Skills

• ELT / Data Pipelines: Proven, hands-on experience building, optimizing, and monitoring production ELT/ETL pipelines and data workflows.

• SQL & Relational Databases: Strong proficiency in SQL (writing complex queries, performance tuning, indexing) for extracting and validating large datasets across relational engines.

• Snowflake: Hands-on experience loading and modeling data in Snowflake using staging strategies, COPY commands, or bulk loading utilities.

• Azure Cloud Storage: Solid background working with Azure Data Lake Storage (ADLS Gen2), Blob Storage, and associated file transfer/ingestion patterns.

• File Processing: Practical experience with bulk file ingestion formats (CSV, Parquet, JSON) and file movement tooling.

• Agile Execution: Ability to deliver rapid, high-quality results within structured project timelines.

Preferred Skills

• Experience with orchestration and transformation tools (e.g., Airflow, dbt, Azure Data Factory, or Python-driven pipeline movers).

• Familiarity with enterprise or industrial data integration platforms and SAP.

• Version control using Git.