Azure Data Engineer

We are seeking a highly skilled Data Engineering specialist with 8+ years of experience with below mentioned Primary Skills to join our dynamic team. The ideal candidate should be passionate about working on Data Engineering on Azure cloud with strong focus on DevOps practices in building product for our customers. Effectively Communicate and Collaborate with internal teams and customer to build code leveraging or building low level design documents aligning to standard coding principles and guidelines.

Key Technical Skills:

1. 5+ years of experience in Azure Databricks with PySpark.
2. 4+ years of experience in Databricks workflow & Unity catalog.
3. 4+ years of experience in ADF (Azure Data Factory).
4. 4+ years of experience in ADLS Gen 2.
5. 4+ years of experience in Azure SQL.
6. 4+ years of experience in Azure Cloud platform.
7. 3+ years of experience in Python programming & package builds.

Required Skills & Experience:

1. 1. Data management experience handling Analytics workload covering design, development, and maintenance of Lakehouse solutions sourcing data from platforms such as ERP sources, API sources, Relational stores, NoSQL and on-prem sources using

   Databricks/PySpark

   as distributed /big data management service, supporting batch and near-real-time ingestion, transformation, and processing.
2. Ability to optimize Spark jobs and manage large-scale data processing using RDD/DataFrame APIs. Demonstrated expertise in partitioning strategies, file format optimization (Parquet/Delta), and Spark SQL tuning. Familiarity with Databricks runtime versions, cluster policies, libraries, and workspace management.
3. Skilled in governing and manage data access for Azure Data lakehouse with

   Unity Catalog

   . Experience in configuring data permissions, object lineage, and access policies with Unity Catalog. Understanding of integrating Unity Catalog with Azure AD, external metastores, and audit trails.
4. Experience in building efficient orchestration solutions using

   Azure data factory, Databricks Workflows.

   Ability to design modular, reusable workflows using tasks, triggers, and dependencies. Skilled in using dynamic expressions, parameterized pipelines, custom activities, and triggers, Familiarity with integration runtime configurations, pipeline performance tuning, and error handling strategies.
5. Strong experience in implementing secure, hierarchical namespace-based data lake storage for structured/semi-structured data, aligned to bronze-silver-gold layers with

   ADLS Gen2.

   Hands-on experience with lifecycle policies, access control (RBAC/ACLs), and folder-level security. Understanding of best practices in file partitioning, retention management, and storage performance optimization.
6. Capable of developing T-SQL queries, stored procedures, and managing metadata layers on

   Azure SQL.

7. Comprehensive experience working across the

   Azure ecosystem,

   including networking, security, monitoring, and cost management relevant to data engineering workloads. Understanding of VNets, Private Endpoints, Key Vaults, Managed Identities, and Azure Monitor. Exposure to DevOps tools for deployment automation (e.g., Azure DevOps, ARM/Bicep/Terraform).
8. Experience in writing modular, testable

   Python

   code used in data transformations, utility functions, and packaging reusable components. Familiarity with Python environments, dependency management (pip/Poetry/Conda), and packaging libraries. Ability to write unit tests using PyTest/unit test and integrate with CI/CD pipelines.
9. Lead solution design discussions, mentor junior engineers, and ensure adherence to coding guidelines, design patterns, and peer review processes. Able to prepare Design documents for development and guiding the team technically. Experience preparing technical design documents, HLD/LLDs, and architecture diagrams. Familiarity with code quality tools (e.g., SonarQube, pylint), and version control workflows (Git).
10. Demonstrates strong verbal and written communication, proactive stakeholder engagement, and a collaborative attitude in cross-functional teams. Ability to articulate technical concepts clearly to both technical and business audiences. Experience in working with product owners, QA, and business analysts to translate requirements into deliverables.