Architect

Role Overview

As a Senior Data Engineer / Tech Lead, you will design, develop, and optimize scalable, enterprise-grade cloud-native data platforms and pipelines on the Microsoft Azure ecosystem . You will possess deep technical ownership and be responsible for implementing modern data architectures (such as Lakehouse and Medallion architecture) . This role requires close collaboration with architects, business analysts, and downstream data consumers to translate complex business requirements into high-performing and secure data solutions.

Key Responsibilities

1.Data Pipeline Design & Orchestration (ADF)

• Design, build, and maintain complex, scalable ETL/ELT pipelines using Azure Data Factory (ADF) to ingest heterogeneous data from diverse sources (APIs, SFTP, Oracle Fusion, SQL Server, Event Hubs, etc.)
• Implement parameter-driven dynamic pipelines, custom activities, and robust scheduling triggers
• Configure and manage Integration Runtimes (Self-Hosted and Azure IR) for secure and efficient on-premise to cloud data movement

2.Advanced Big Data Processing (Azure Databricks)

• Develop high-performance batch and real-time streaming data pipelines using Azure Databricks, PySpark, Spark SQL, or Scala
• Implement Medallion Architecture (Bronze, Silver, and Gold layers) and maintain modern Lakehouse architectures utilizing Delta Lake
• Optimize Spark compute usage through parameter-driven jobs, proper cluster scaling, and Databricks serverless compute configurations
• Perform deep performance tuning, query optimization, and memory management of heavy Spark workloads

3.Data Warehousing & Modeling

• Design and refine data models including dimensional modeling, star schemas, and Slowly Changing Dimensions (SCDs)
• Optimize databases by writing complex, performant T-SQL queries, stored procedures, and schemas on Azure SQL Database or Azure Synapse Analytics

4.Data Governance, Security & DevOps

• Enforce data governance, lineage, and access controls across the platform utilizing Unity Catalog .
• Secure data architectures utilizing Azure Key Vault, Managed Identities, Service Principals, and VNET-integrated environments
• Implement CI/CD pipelines using Git and Azure DevOps to automate database and pipeline deployments

5.Leadership & Collaboration

• Provide technical mentorship and guidance to junior developers and QA teams
• Enforce engineering discipline through standard code reviews, structured pre/post-deployment validations, and thorough documentation

Required Skills & Qualifications

• Overall Experience: Minimum of 8+ years of professional experience in Data Engineering or Data Warehousing
• Azure Experience: 4+ years of dedicated, hands-on experience designing and operating modern data platforms on Microsoft Azure
• Programming Languages: Proficient in Python/PySpark, SQL (advanced query optimization, indexing, stored procedures), and optionally Scala
• Core Azure Stack: Extensive knowledge of Azure Data Factory, ADLS Gen2, Azure Databricks (Delta Lake), Azure SQL Database, and Azure Synapse Analytics
• Big Data Concepts: Robust understanding of Apache Spark architecture, distributed computing, and the Hadoop ecosystem
• Data Integration: Strong background in CDC (Change Data Capture) mechanisms, API integrations, and event-driven data streaming (e.g., Event Hubs/Kafka)
• Soft Skills: Excellent analytical capabilities, strong documentation skills, and the ability to articulate technical strategies to non-technical business partners

Preferred / "Good-to-Have" Qualifications

• Certifications: Microsoft Certified: Azure Data Engineer Associate (DP-203), Azure Solutions Architect Expert, or Databricks Certified Associate/Professional
• Cloud Platforms: Exposure to multi-cloud environments (e.g., Google Cloud Platform or AWS)
• IaC: Experience using Infrastructure as Code (IaC) tools like Terraform or ARM Templates to deploy Azure resources