Azure Data Engineering Specialist / Lead

Azure Data Engineering Specialist / Lead

Full Time

Remote

We are seeking a highly skilled Data Engineering Specialist to join our dynamic team who are at the forefront of enabling enterprises in healthcare sectors. 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, collaborate with internal teams and customer to build code leveraging or building low level design documents aligning to standard coding principles and guidelines.

Skills/Experience

• 5+ years of experience in Azure Cloud platform and 4+ years of experience in Azure Databricks with PySpark
• 3+ years of experience in ADF (Azure Data Factory), ADLS Gen 2 and Azure SQL
• 2+ years of experience in Databricks workflow & Unity catalog
• 2+ years of experience in Python programming & package builds
• Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps
• Provide constructive feedback during code reviews and be open to receiving feedback on your own code
• Problem-Solving and Analytical Thinking, Capability to troubleshoot and resolve issues efficiently
• Provides regular updates, proactive and due diligent to carry out responsibilities
• Communicate effectively with internal and customer stakeholders
• Communication approach: verbal, emails and instant messages
• Strong interpersonal skills to build and maintain productive relationships with team members

Secondary Skills

• Good to have Azure Entra/AD skills and GitHub Actions
• Good to have orchestration experience using Airflow, Dagster, LogicApp
• Good to have experience working on event-driven architectures using Kafka, Azure Event Hub
• Good to have experience in managing Cloud storage solutions on Azure Data Lake Storage (ADLS)
• Good to have exposure on Google Cloud Pub/Sub; Experience with Google Cloud Storage will be an advantage
• Good to have experience developing and maintaining Change Data Capture (CDC) solutions preferably using Debezium
• Good to have hands-on experience on data migration projects specifically involving Azure Synapse and Databricks Lakehouse

Job / Role Description

• 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
• 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
• 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)
• Skilled in governing and manage data access for Azure Data Lakehouses 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
• Lead solution design discussions, mentor juniors, 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)
• 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
• 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
• Capable of developing T-SQL queries, stored procedures, and managing metadata layers on Azure SQL; 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
• 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