🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Job Description : We are seeking a highly skilled and experienced Data Engineer to join our growing data team. In this role, you will lead the design and implementation of scalable, high-performance data pipelines using Snowflake, dbt, and Matillion Data Productivity Cloud, define architectural best practices, and drive data transformation at scale. You will work closely with clients to translate business needs into robust data solutions and play a key role in mentoring junior engineers, enforcing standards, and delivering production-grade data platforms. Key Responsibilities : - Architect and implement modular, test-driven ELT pipelines using dbt and Matillion Data Productivity Cloud on Snowflake. - Design layered data models (e.g., staging, intermediate, mart layers / medallion architecture) aligned with dbt and Matillion best practices. - Lead ingestion of structured and semi-structured data from APIs, flat files, cloud storage (Azure Data Lake, AWS S3), and databases into Snowflake using Matillions native connectors and custom components where required. - Optimize Snowflake for performance and cost : warehouse sizing, clustering, materializations, query profiling, and credit monitoring. - Apply advanced dbt capabilities including macros, packages, custom tests, sources, exposures, and documentation using dbt docs. - Build reusable Matillion pipelines, shared jobs, and orchestration flows on the Data Productivity Cloud, integrating them with dbt transformations for end-to-end data delivery. - Orchestrate workflows using dbt Cloud, Matillion Data Productivity Cloud, Airflow, or Azure Data Factory, integrated with CI/CD pipelines. - Define and enforce data governance and compliance practices using Snowflake RBAC, secure data sharing, and encryption strategies. - Collaborate with analysts, data scientists, architects, and business stakeholders to deliver validated, business-ready data assets. - Mentor junior engineers, lead architectural/code reviews, and help establish reusable frameworks and standards across dbt, Matillion, and Snowflake. - Engage with clients to gather requirements, present solutions, and manage end-to-end project delivery in a consulting setup. Required Qualifications : - 3 to 5 years of experience in data engineering roles, with 3+ years of hands-on experience working with Snowflake and dbt in production environments, and demonstrable project experience with Matillion (Data Productivity Cloud or Matillion ETL). Technical Skills : 1. Cloud Data Warehouse & Transformation Stack : - 1. Expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management. - 2. Experience in dbt development : modular model design, macros, tests, documentation, and version control using Git. - 3. Hands-on experience with Matillion Data Productivity Cloud, including Designer pipelines, orchestration jobs, transformation jobs, variables, shared jobs, and Git-based project management. 2. Orchestration and Integration : - 1. Proficiency in orchestrating workflows using dbt Cloud, Matillion Data Productivity Cloud, Airflow, or Azure Data Factory. - 2. Comfortable working with data ingestion from cloud storage (e.g., Azure Data Lake, AWS S3) and APIs, including building Matillion pipelines that consume REST APIs and leverage native SaaS connectors. 3. Data Modelling and Architecture : - 1. Dimensional modelling (Star/Snowflake schemas), Slowly changing dimensions. - 2. Knowledge of modern data warehousing principles. - 3. Experience implementing Medallion Architecture (Bronze/Silver/Gold layers) using a combination of Matillion for ingestion and staging and dbt for downstream transformations. - 4. Experience working with Parquet, JSON, CSV, or other data formats. 4. Programming Languages : - 1. Python : For data transformation, notebook development, automation, and extending Matillion via Python script components. - 2. SQL : Strong grasp of SQL for querying and performance tuning. - 3. Jinja (nice to have) : Exposure to Jinja for advanced dbt development. 5. Data Engineering & Analytical Skills : - 1. ETL/ELT pipeline design and optimization across dbt and Matillion. - 2. Exposure to AI/ML data pipelines, feature stores, or MLflow for model tracking (good to have). - 3. Exposure to data quality and validation frameworks. 6. Security & Governance : - 1. Experience implementing data quality checks using dbt tests and Matillion validation components. - 2. Data encryption, secure key management, and security best practices for Snowflake, dbt, and Matillion (credential management, OAuth, workspace/environment isolation). Soft Skills & Leadership : - Ability to thrive in client-facing roles with competing/changing priorities and quick-paced delivery cycles. - Stakeholder .
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.