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Job Summary We are looking for a highly skilled Senior Data Engineer with 6 to 9 years of hands-on experience in SQL, Python for data engineering, Azure data engineering services, and Snowflake analytical layer development. The ideal candidate should have strong expertise in designing, developing, optimizing, and maintaining enterprise-grade data pipelines, scalable data warehouses, data marts, and analytical models. The candidate should be capable of leading technical implementations, mentoring junior engineers, collaborating with architects and business stakeholders, and driving best practices across data engineering initiatives. Experience with DBT, CI/CD, DevOps practices, and performance optimization is highly preferred. Key Responsibilities Core Duties Understand business requirements, architecture documents, and translate them into scalable technical solutions. Lead end-to-end development of Azure Data Factory (ADF) pipelines and enterprise data workflows. Design and develop scalable ELT/ETL frameworks using Azure services and Snowflake. Build and optimize analytical layers, dimensional models, data marts, and semantic data structures in Snowflake. Create reusable, maintainable, and performance-optimized SQL and Python-based data transformation frameworks. Drive data quality, validation, reconciliation, and governance processes across data platforms. Conduct detailed code reviews, design reviews, and ensure engineering best practices are followed. Troubleshoot complex production issues, optimize pipeline performance, and reduce data processing latency. Participate actively in solution architecture discussions and provide technical recommendations. Lead technical demos, walkthroughs, sprint reviews, and stakeholder presentations. Mentor junior and mid-level engineers and guide them in technical problem-solving. Contribute to framework development, automation initiatives, and process standardization. Support release management, CI/CD deployment processes, and environment migrations. Collaborate with cross-functional teams including business analysts, architects, QA teams, DevOps, and product owners. Technical Skills or Knowledge Areas Mandatory Skills Advanced SQL (complex joins, CTEs, window functions, query optimization, stored procedures) Python for data engineering and automation Azure Data Engineering Services Azure Data Factory (ADF) Azure Data Bricks Azure Data Lake Azure Functions Azure Synapse Analytics Logic Apps Key Vault Snowflake Data Warehouse Development Data Modeling (Star Schema, Snowflake Schema, Dimensional Modeling) Performance tuning and optimization CI/CD and DevOps for data engineering Git/version control systems Preferred Skills DBT (Data Build Tool) Apache Spark / PySpark Airflow Terraform or Infrastructure as Code Data governance and metadata management API integration and real-time data ingestion Monitoring and observability frameworks Collaboration & Leadership Strong experience working in offshore-onshore delivery models. Ability to lead technical discussions and coordinate across multiple teams. Familiarity with Agile/Scrum methodologies. Hands-on experience with: Slack JIRA/Kanban Confluence Azure DevOps Ability to communicate effectively with technical and non-technical stakeholders. Provide mentorship and technical guidance to junior team members. Drive collaboration and foster a positive engineering culture. Process Improvement Identify opportunities for automation, optimization, and engineering standardization. Improve reusable framework components and reduce operational overhead. Participate in architecture reviews and suggest scalable design improvements. Handle change requests, impact analysis, and production support activities efficiently. Contribute to engineering documentation standards and knowledge-sharing initiatives. Compliance & Documentation Create and maintain High-Level Design (HLD) and Low-Level Design (LLD) documents. Prepare technical architecture diagrams and data flow documentation. Create operational runbooks, support documentation, and stakeholder training materials. Ensure compliance with enterprise security, governance, and audit requiremen .
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.