Data Engineer

Neara; India; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Neara is seeking an experienced Data Engineer to support a high‑priority data integration and cloud enablement initiative. The role focuses on designing and executing end‑to‑end ELT pipelines, migrating data from SQL sources into Snowflake and orchestrating bulk file transfers into ADLS.

You will work closely with the Lead Data Integration Expert and client teams to ensure secure, governed connectivity and scalable data workflows across cloud storage and analytics layers.

Qualifications

• Experience building production ELT/ETL pipelines.
• Strong SQL skills for large data loads.
• Snowflake data modeling and COPY/bulk loading.
• Experience with ADLS Gen2 and Azure storage.
• Familiarity with Airflow, dbt, or ADF.

Responsibilities

• Design, build, and maintain scalable ELT/ETL pipelines and data workflows for ingestion and transformation.
• Execute structured data extraction, movement, and landing from SQL databases 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, 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.

Skills

ELT / Data Pipelines

SQL & Databases

Snowflake

Azure Data Lake Storage

File Processing

Agile Execution

Tools

Airflow

dbt

Azure Data Factory

Python

Git

SAP

Job description

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).

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

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.