Data Engineer - Snowflake AND Azure

USEReady Inc.; Mohali, Gurugram District, Bengaluru; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

USEReady Inc. is seeking an experienced Data Engineering Lead to design and execute end-to-end ELT pipelines, migrating data from SQL sources into Snowflake and orchestrating bulk file transfers into Azure Data Lake Storage (ADLS).

You will collaborate with client teams to ensure connectivity, security, and governance across cloud data platforms. The role emphasizes hands-on development of scalable ELT/ETL workflows, optimized SQL, and robust data quality checks within an agile delivery

Qualifications

• Experience designing and running production ELT/ETL pipelines.
• Strong SQL and data modeling skills with performance tuning.
• Hands-on Snowflake loading and data modeling experience.
• Experience with ADLS Gen2 and data transfers.
• Experience with bulk file ingestion formats.
• Agile delivery experience.

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 transfer files into Azure Data Lake Storage (ADLS).
• Write and tune high-performance SQL queries for data modeling, validation, and staging transformations.
• Conduct data reconciliation and completeness checks to ensure zero loss across data pipelines during bulk migration.
• Collaborate with the Lead Data Integration Expert and client engineering teams on connectivity, security, and governance.

Skills

ELT / Data Pipelines

SQL & Relational Databases

Snowflake

Azure Data Lake Storage (ADLS)

File Processing

Agile Execution

Tools

Airflow

dbt

Azure Data Factory

Python

Git

Job description

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

Role OverviewWe are seeking an experienced Data Engineering Lead 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 skills.