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Job Purpose Design and deliver data transformation pipelines, semantic models, and BI solutions that enable reliable, self-service analytics across the organization. The role bridges data engineering and business intelligence, owning the analytical data layer from source transformation through to governed, business-ready reporting assets. Key Result Responsibilities Design and maintain modular, well-tested ELT pipelines using tools such as dbt, Azure Data Factory, or equivalent orchestration frameworks.Build and maintain the semantic/analytical data layer in Snowflake or Microsoft Fabric, including fact and dimension tables, conformed metrics, and reusable dbt models.Develop production-grade Power BI reports and dashboards, including well-structured data models, DAX measures, and row-level security configurations.Define and implement data quality rules, testing frameworks, and monitoring to ensure accuracy and consistency of analytics outputs.Key Result Responsibilities-Continued Collaborate with data analysts, business stakeholders, and data engineers to translate reporting requirements into robust, governed data assets.Apply and enforce dimensional modelling principles (star schema, slowly changing dimensions) to support efficient BI consumption.Work within Azure and Snowflake environments to manage datasets, optimize query performance, and control data access.Maintain clear documentation for all pipelines, semantic models, metric definitions, and report logic.Participate actively in code reviews and contribute to improving team standards for analytics engineering.Qualifications (Academic, training, languages) Bachelor's degree in Computer Science, Information Technology, Business Analytics, Statistics, or a related field. Fluent in English Language.ITIL Certification is an advantage but not mandatory.Strong SQL skills and solid working knowledge of Python for data transformation tasks.Working knowledge of dbt for transformation layer development, including tests, documentation, and lineage.Hands-on experience with Power BI data modelling, DAX, report design, and workspace governance.Demonstrable experience delivering Power BI solutions in a professional setting.Familiarity with Microsoft Fabric or Azure Data Factory for pipeline orchestration and data movement.Solid understanding of dimensional modelling (star/snowflake schema, SCD types).Understanding of BI governance principles semantic model management, certified datasets, and access control in Power BI.Work Experience With 24 years of hands-on experience in analytics engineering, data engineering, or BI development.Experience with Snowflake or Azure Synapse Analytics for data warehousing and query optimization.Experience with Git-based version control and collaborative development workflows. .
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.