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Job Description DBT & Snowflake Engineer Position DBT & Snowflake Engineer / Analytics Engineer Role Overview We are looking for an experienced DBT & Snowflake Engineer to design, develop, and maintain scalable data transformation pipelines and analytical data models using dbt and Snowflake. The candidate will work closely with Data Engineering, Data Architecture, BI, QA, and Product teams to build high-quality, scalable, and performant data solutions that support enterprise reporting and analytics. Key Responsibilities Develop and maintain dbt models on the Snowflake data platform. Design scalable staging, intermediate, and data mart models using dbt. Develop complex and optimized SQL queries in Snowflake. Implement incremental models, snapshots, macros, seeds, sources, and dbt tests. Design and implement dimensional data models, including fact and dimension tables. Optimize Snowflake queries, warehouses, and data models for performance and cost efficiency. Implement data quality checks, reconciliation, and validation using dbt tests and Snowflake. Establish and maintain data lineage, documentation, and model dependencies. Troubleshoot data discrepancies across source systems, Snowflake, dbt transformations, and reporting datasets. Work with Data Engineers and Architects to define appropriate ELT and data transformation strategies. Support integration of Snowflake data models with Power BI and other BI platforms. Implement and maintain CI/CD processes for dbt using Git/GitHub/Azure DevOps. Participate in code reviews and ensure adherence to SQL, dbt, Snowflake, and data engineering best practices. Monitor production transformations and proactively resolve data pipeline and data-quality issues. Create and maintain technical documentation, data dictionaries, and runbooks. Mandatory Technical Skills DBT Strong hands-on experience with dbt Core or dbt Cloud. Experience with dbt models, sources, tests, snapshots, macros, seeds, and incremental processing. Understanding of dbt project structure, dependencies, exposures, and documentation. Experience implementing dbt CI/CD and Git-based development workflows. Snowflake Strong hands-on experience with Snowflake. Advanced SQL development and query optimization. Good understanding of Snowflake architecture and compute/storage separation. Experience with virtual warehouses, databases, schemas, stages, views, tables, and secure views. Experience with Snowflake performance and cost optimization. Understanding of Snowflake features such as Time Travel, Zero-Copy Cloning, Streams/Tasks, and Snowpipe is preferred. Experience designing analytical data models within Snowflake. Data Engineering / SQL Strong SQL skills with experience handling complex transformations and large datasets. Strong understanding of ETL/ELT architecture. Experience with dimensional modeling, star schema, and data marts. Strong understanding of data quality, validation, reconciliation, and data governance. Preferred Skills Experience with Power BI and enterprise BI reporting. Experience with Python for automation or data engineering. Experience with GitHub / Azure DevOps and CI/CD pipelines. Experience with enterprise-scale Data Warehouse / Datamart implementations. Knowledge of performance tuning and cost optimization in cloud data platforms. Experience working in Agile/Scrum environments. Experience 47+ years of experience in Data Engineering, Analytics Engineering, or Data Warehousing, with strong hands-on experience in Snowflake and dbt. Key Competencies Strong analytical and problem-solving skills. Ability to translate business requirements into scalable data models. Strong focus on data quality and accuracy. Good understanding of enterprise data architecture. Strong communication and collaboration skills. Ability to work effectively with Data Engineering, BI, QA, Architecture, and Product teams. .
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.
