Senior Data Engineer(Azure)

We are seeking a highly skilled and motivated Data Engineer to join our data team. The ideal

candidate will have a strong foundation in cloud data architectures, specifically within cloud

environments (Azure preferably) or any equivalent (GCP/AWS)**. You will play a pivotal role in

designing, building, and maintaining scalable data pipelines, ensuring data integrity, and

facilitating seamless data migration from various legacy sources to modern cloud platforms.

Your primary focus will be on SQL proficiency and data integration using tools like Azure Data

Factory (ADF), Google Cloud Dataflow, or Cloud Composer, supported by a solid

understanding of data modeling principles.

Key Responsibilities

Mandatory Qualifications

ETL/ELT Pipeline Development: Design, develop, and maintain robust data pipelines

using Azure Data Factory (ADF), Google Cloud Dataflow, or Cloud Composer (Airflow)

to ingest, transform, and load data from diverse sources (on-premise databases, APIs, flat

files) into our data warehouse/data lake.

Data Migration: Lead and execute complex data migration projects, moving data from

legacy systems to cloud-based solutions while ensuring zero data loss and minimal

downtime.

Data Modeling: Design and implement efficient data models (Star schema, Snowflake

schema, Data Vault) to support business intelligence and analytics requirements.

SQL Optimization: Write complex, highly optimized SQL queries for data transformation,

validation, and analysis. Performance tune existing queries within Azure PostgreSQL or

equivalent.

Cloud Infrastructure: Architect and manage data storage solutions using Azure and GCP

services (Azure PostgreSql, Azure Data Lake Gen2).

Scripting & Automation: Utilize Python (or other scripting languages) for data

manipulation, automation of routine tasks, and bridging gaps in standard ETL tool

capabilities.

Data Quality & Governance: Implement data quality checks and validation processes to

ensure accuracy and consistency across the data lifecycle.

Collaboration: Work closely with Data Scientists, Analysts, and business stakeholders to

understand data requirements and translate them into technical solutions. Also familiarity to

use AI-assisted tooling for day-to-day work and automations.

Strong SQL Skills: Expert-level proficiency in SQL is non-negotiable. Ability to write

complex queries, analyze query performance, and manage database schemas

Preferred Qualifications (Nice to Have)

(PostgreSql).

Cloud Data Integration: Proven experience with Azure Data Factory (ADF) OR Google

Cloud Dataflow/Composer, and equivalent enterprise ETL tools (e.g., Informatica Cloud,

Matillion, Talend) in a cloud environment.

Cloud Familiarity: Hands-on experience with cloud platforms (Azure and/or GCP) and

understanding of cloud storage, compute, and networking concepts.

Data Migration Experience: Demonstrated history of successfully migrating data from

heterogeneous data sources (e.g., SQL Server, Oracle, PostgreSQL, On-prem files) to the

cloud.

Communication: Excellent verbal and written communication skills. Ability to explain

complex technical concepts to non-technical stakeholders clearly.

Scripting: Proficiency in Python (Pandas, PySpark, Apache Beam) for data processing.

CI/CD: Familiarity with DevOps practices for data pipelines (Azure DevOps, Cloud Build,

GitHub Actions).

Certifications: Any relevant Data Engineering certification, such as Microsoft Certified:

Azure Data Engineer Associate or Google Cloud Professional Data Engineer.