USEReady helps enterprises apply AI and agentic intelligence to improve decisions, automate operations, and build smarter, more autonomous business systems.For more than a decade, we have built the foundations that make this possible by modernizing BI environments, migrating legacy platforms, improving data quality, and enabling governed, cloud-first architectures.

These foundations now support the next step: AI-driven insights, automated intelligence, and agent-powered decision support that reduce complexity and accelerate outcomes.

We work closely with technology leaders such as AWS, Elementum, Snowflake, Tableau, Databricks, and others to help organizations modernize analytics, strengthen governance, and deploy agentic automation with confidence. Headquartered in New York City with 450+ experts across the United States, Canada, India, and Singapore, we serve industries including financial services, healthcare, manufacturing, government, education, and retail. Our deep expertise, player-coach delivery model, and focus on fast, measurable results make us a trusted partner for building an AI-ready enterprise.

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

1. Design, build, and maintain scalable ELT/ETL pipelines and data workflows for ingestion and transformation.
2. Execute structured data extraction, movement, and landing from relational SQL databases directly into Snowflake staging and core layers.
3. Build,



execute, and monitor file movement tasks to efficiently transfer flat files, logs, or unstructured formats into Azure Data Lake Storage (ADLS).
4. Write and tune high-performance SQL queries for data modeling, validation, data verification, and staging transformations.
5. Conduct data reconciliation and completeness checks to ensure zero loss across data pipelines during bulk migration.
6. Collaborate closely with the Lead Data Integration Expert and client engineering teams to align with platform connectivity, security, and governance protocols.

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

Required Skills

- ELT / Data Pipelines: Proven, hands-on experience building, optimizing, and monitoring production ELT/ETL pipelines and data workflows.
- SQL & Relational Databases: Solid 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.

📌 Data Engineer (Bengaluru)
🏢 USEReady
📍 Bengaluru