Job Description
Who we are
We are seeking a detail-oriented Snowflake Data Analyst to support enterprise data migration and modernisation programmes. The successful candidate will have strong experience using Snowflake for data analysis and querying, with proven expertise in data reconciliation, migration validation, and data quality assurance.
Working alongside Data Architects, Business Analysts, and business stakeholders, you will be responsible for validating that migrated data is complete, accurate, and aligned with business expectations. You will perform detailed source-to-target reconciliation, investigate data discrepancies, execute data quality checks, and provide assurance that migration activities meet agreed business and regulatory requirements.
This role is ideal for an experienced data analyst with a background in data migration who enjoys analysing complex datasets and ensuring trusted, high-quality data.
What you'll be doing
- Client delivery and stakeholder engagement
- Data analysis, migration, and quality assurance
- Governance and best practice adoption
- Cross-functional collaboration
- Key Stakeholders: Solution Architects, Data Engineers, Developers, and Analysts
- Senior client stakeholders across Data and BI functions
What you'll bring along
- BSc/MSc in Computer Science, Data Engineering, or related field; Snowflake certification(s) strongly desirable
- Experience: Minimum 5–8 years in data engineering, data warehousing, or data architecture roles, with at least 2+ years working with Snowflake
- Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon)
- Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines
- Understanding of data governance, security, and compliance frameworks
- Strong hands-on experience querying data in Snowflake
- Advanced SQL skills for analysis, reconciliation, validation, and reporting
- Experience performing source-to-target data reconciliation
- Experience with enterprise data migration and migration assurance
- Strong understanding of data quality principles and validation techniques
- Familiarity with data lake architectures and distributed processing frameworks (e.g., Spark, Hadoop)
