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Why Valtech Were advisors, visionaries, creative and techies. We embrace all things digital. We talk to each other. We have fun. We love our clients. Were looking ahead We are global Join our innovative, fast-growing data team at the forefront of cloud data architecture on Microsoft Azure. We're building scalable, secure, and modern data platforms using cutting-edge Azure services and Databricks unified analytics platform. If you're passionate about creating high-performance data infrastructure and solving complex big data challenges in a cloud-native environment, this is the perfect opportunity for you. As a Senior Data Engineer specializing in Azure and Databricks, you will architect and implement enterprise-grade cloud-native data solutions on the Microsoft Azure ecosystem. This is a hands-on engineering role with significant architectural influence, where you'll work extensively with Azure Data Factory, Databricks, Delta Lake, and other modern data tools to create efficient, maintainable, and scalable data pipelines using medallion architecture and lakehouse patterns. What you do 47 years of hands-on experience in data engineering with strong focus on cloud data platforms. Proven experience with big data technologies and distributed computing Technical Expertise Azure Mastery: Deep expertise in Azure data services including Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, and Azure SQL Database Databricks Proficiency: Advanced proficiency in Databricks including cluster management, notebook development, and Delta Lake Big Data Processing: Strong experience with Apache Spark (PySpark, Scala), distributed computing concepts, and performance optimization Programming: Proficiency in Python, Scala, and SQL for data processing and transformation Data Architecture Experience with lakehouse architecture, medallion architecture, and modern data warehouse design patterns Streaming Technologies: Knowledge of real-time data processing using Azure Event Hubs, Kafka, and Spark Streaming Cloud Platforms: Deep understanding of Microsoft Azure ecosystem and native data services Data Management & DevOps Data Governance: Understanding of data governance frameworks, Unity Catalog, and data quality practices DevOps: Experience with CI/CD practices, Git-based workflows, and Infrastructure as Code Orchestration: Knowledge of data orchestration tools like Azure Data Factory, Airflow, or similar platforms Security: Understanding of Azure security best practices, RBAC, and data encryption Advanced Technologies Experience with Azure Machine Learning and MLOps practices Knowledge of containerization (Docker, Kubernetes) and Azure Container Instances Experience with Power BI for data visualization and reporting Understanding of data mesh architecture and domain-driven design principles Experience with Azure DevOps for CI/CD pipeline management Familiarity with monitoring tools like Azure Monitor, Application Insights, and Databricks monitoring Specialized Skills Experience with real-time analytics and complex event processing Knowledge of graph databases and Azure Cosmos DB Understanding of data lake optimization techniques and performance tuning Experience with multi-cloud or hybrid cloud architectures Technology Stack Core Azure Services Data Storage: Azure Data Lake Storage Gen2, Azure Blob Storage, Azure SQL Database Data Processing: Databricks, Azure Synapse Analytics, Azure Data Factory Streaming: Azure Event Hubs, Azure Stream Analytics, Kafka Analytics: Databricks SQL, Azure Analysis Services, Power BI Machine Learning: Azure Machine Learning, MLflow, Databricks ML Big Data & Processing Compute: Apache Spark (PySpark, Scala), Databricks Runtime Data Formats: Delta Lake, Parquet, JSON, Avro Architecture: Medallion Architecture, Lakehouse, Data Mesh Orchestration: Azure Data Factory, Airflow, Azure Logic Apps Development & DevOps Programming: Python, Scala, SQL, PowerShell Version Control: Git, Azure DevOps, GitHub Infrastructure: ARM Templates, Terraform, Azure Resource Manager Monitoring: Azure Monitor, Application Insights, Databricks monitoring Security & Governance Security: Azure Active Directory, Key Vault, RBAC Governance: Unity Catalog, Azure Purview, Data Lineage Compliance: GDPR, data encryption, access controls Certifications Microsoft Azure Data Engineer Associate (DP-203) Databricks Certified Associate Developer for Apache Spark Databricks Certified Professional Data Engineer Azure Solutions Architect or Azure Data Fundamentals certifications What we ask Data Architecture & Engineering Design and implement enterprise-scale, robust data solutions using Azure and Databricks Architect scalable ELT/ETL pipelines using Azure Data Factory, Azure Synapse Analytics, and Databricks Build automated data ingestion processes from various sources including streaming and batch data Develop and maintain data .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.