At a Glance
- Tasks: Build scalable data pipelines and develop dimensional data models in a cloud-native environment.
- Company: Join a forward-thinking tech company focused on data innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team culture with a focus on continuous improvement and learning.
- Why this job: Make an impact by modernising data solutions and collaborating with top engineers.
- Qualifications: 3+ years in Data Engineering with strong SQL and Snowflake experience.
The predicted salary is between 63000 - 77000 £ per year.
Job Description
- We are looking for a hands-on Snowflake Data Engineer to support the modernization of an enterprise data warehouse to a cloud-native analytics platform on AWS and Snowflake.
- You will be responsible for building scalable data pipelines, developing dimensional data models, and delivering reliable ELT solutions while working closely with architects, senior engineers, QA teams, cloud engineers, and business stakeholders.
Key Responsibilities
- Design, develop, and maintain data pipelines and warehouse layers (RAW, ODS, DW) in Snowflake.
- Build and support ELT/ETL pipelines using Snowflake features such as Snowpipe, Tasks, Streams, Stages, Storage Integrations, and Stored Procedures.
- Develop dimensional data models including fact tables, dimension tables, aggregates, and lookup tables.
- Write, optimize, and troubleshoot complex SQL queries for transformation, reconciliation, and performance tuning.
- Implement data quality checks, validation, reconciliation, logging, and error-handling mechanisms.
- Support rerun, recovery, and replay processes for failed data loads.
- Integrate data securely using AWS services such as S3 and IAM.
- Participate in SIT, UAT, deployment, documentation, and production support.
- Collaborate with architects and engineering teams while following coding standards and best practices.
- Contribute to code reviews and continuous improvement initiatives.
- Required Skills & Experience
- 3+ years of experience in Data Engineering, Data Warehousing, or Database Development.
- Hands-on experience with Snowflake or another cloud data warehouse platform.
- Strong SQL skills including joins, window functions, CTEs, query optimization, and troubleshooting.
- Good understanding of dimensional modelling (Star Schema, Fact & Dimension tables).
- Experience developing and maintaining ETL/ELT pipelines.
- Knowledge of data quality, reconciliation, logging, monitoring, and production support.
- Good analytical, communication, and problem-solving skills.
- Ability to work independently and collaboratively in an Agile delivery environment.
- Preferred Skills
- Experience with Snowflake features: Snowpipe Tasks Streams Stages Storage Integrations Stored Procedures Experience migrating Oracle Data Warehouse or legacy platforms to Snowflake.
- Experience handling semi-structured data (JSON, XML, VARIANT).
- AWS knowledge, especially: Amazon S3 IAM Encryption Cloud integration Experience with Git, CI/CD, and deployment automation.
- Exposure to Tableau or other BI reporting tools. Snowflake, AWS, Data Engineering, AI/ML, or Gen AI certifications
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Data Engineer employer: ELLIOTT MOSS CONSULTING PTE. LTD.
ELLIOTT MOSS CONSULTING PTE. LTD. is an exceptional employer that fosters a collaborative and innovative work culture, perfect for those passionate about AI and technology. Located in a vibrant tech hub, employees benefit from continuous growth opportunities, access to cutting-edge resources, and a supportive environment that encourages creativity and professional development. Join us to be part of a forward-thinking team dedicated to solving real-world challenges with advanced AI solutions.
Contact Details:
ELLIOTT MOSS CONSULTING PTE. LTD. Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
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We think you need these skills to ace Data Engineer
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
