USEReady Technology - Data Engineer - ADLS/Snowflake DB

USEReady; Kolkata District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

USEReady is seeking an experienced Data Engineer to design and implement end-to-end ELT pipelines, migrating data from SQL databases into Snowflake and coordinating bulk transfers to Azure Data Lake Storage. You will build scalable data workflows and optimize SQL-based transformations.

The role requires hands-on experience with Snowflake, ADLS, and modern orchestration tools, and a collaborative, Agile mindset to deliver quickly within contract timelines.

Qualifications

• Experience designing, building and monitoring production ELT/ETL pipelines and data workflows.
• Strong SQL skills for data extraction, validation, and staging transformations.
• Snowflake data loading, modeling, staging, and bulk loading utilities experience.
• Azure Data Lake Storage (ADLS Gen2) and blob storage familiarity for data transfers.
• Experience with bulk file ingestion formats (CSV, Parquet, JSON) and file movement tooling.
• Ability to deliver high-quality results within structured project timelines.

Responsibilities

• Design end-to-end ELT pipelines to migrate structured data from SQL sources into Snowflake.
• Build and monitor file movement tasks to transfer data to ADLS.
• Write optimized SQL queries for data modeling, validation, and staging transformations.
• Conduct data reconciliation and verify zero data loss across migrations.
• Collaborate with client teams to ensure connectivity, security, and governance compliance.

Skills

ELT pipelines

SQL & relational DBs

Snowflake

Azure Data Lake Storage

File processing

Agile execution

Tools

Airflow

dbt

Azure Data Factory

Python-based pipelines

Git

Job description

Job Description

Role Title: Snowflake Data Engineer Role

Position Type: Contract (3-6 months)

Location: India

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 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.
• Version control using Git.

(ref:hirist.tech)