Create reusable, maintainable, and well-documented data engineering solutions.
Data Integration, Modeling, and Harmonization
Integrate data from multiple sources, including Nielsen, Circana, internal systems, and other business datasets.
Develop scalable data models for reporting, analytics, and business intelligence use cases.
Ensure consistency across product, period, market, customer, and other business dimensions and hierarchies.
Review existing data models and processes to identify sustainable, scalable, and automated improvements.
Harmonize data and processes while balancing speed, quality, and business requirements.
Data Governance, Quality, and Security — 15%
Establish and maintain data-quality rules, validation metrics, monitoring processes, and issue-resolution workflows.
Ensure accurate, complete, consistent, and reliable data flows across the data environment.
Support data governance, stewardship, metadata, documentation, and data-security practices.
Apply appropriate Snowflake security controls, including role-based access, masking policies, and row-access policies.
Proactively identify and resolve data-quality and data-integrity issues.
New Data Asset Integration
Develop an understanding of new datasets requested by business stakeholders.
Assess the structure, quality, and usability of new data sources.
Design and implement processes to integrate new data assets into the existing data environment.
Ensure that new data assets are scalable, governed, documented, and fit for analytics use.
Stakeholder and Analytics Enablement
Understand data requirements from stakeholders and internal teams.
Deliver data transformations and analytical datasets that help answer business questions faster and more effectively.
Support reporting and visualization teams with backend data architecture and data-model development.
Translate business requirements into practical and sustainable technical solutions.
Communicate effectively with stakeholders, delivery teams, and business partners throughout the project lifecycle.
Performance, Scalability, and Cost Optimization
Optimize SQL queries, dbt models, Snowflake workloads, Delta storage formats, and pipeline performance.
Apply performance-tuning techniques across Snowflake, dbt, Databricks, and Azure environments.
Design solutions that support scalability, reliability, maintainability, and cost efficiency.
Monitor data workflows and proactively address performance and operational issues.
Continuous Improvement and Team Contribution
Contribute to continuous-improvement initiatives across data engineering and analytics processes.
Share knowledge, provide peer support, and promote effective engineering practices.
Remain curious and adapt to evolving tools, technologies, and business needs.
Build strong working relationships and contribute positively as a team member.
MINIMUM QUALIFICATIONS
Bachelor’s degree in Computer Science, Information Technology, Electronics and Telecommunications, or a related field.
Minimum 7 years of experience in Data Engineering.
Mandatory experience working with data lakes and multiple data sources.
Strong hands-on experience with - SQL, Snowflake, Snowpipe, Snowflake Streams and Tasks, Dynamic Tables, Stored Procedures, dbt, including models, Jinja templating, macros, tests, and documentation
Strong knowledge of data warehousing, data modeling, and ETL/ELT frameworks.
Experience with large-scale data processing and analytics engineering.
Experience developing data platforms or business intelligence solutions.
Strong understanding of data quality, governance, security, and data-access principles.
Effective communication, stakeholder-management, and problem-solving skills.
Ability to manage ambiguity, make timely decisions, and deliver high-quality work within agreed timelines.
PREFERRED SKILLS
Master’s degree in Computer Science, Information Technology, Electronics and Telecommunications, or a related field.
Experience in the FMCG, consumer goods, retail, or market research industries.
Experience working with Nielsen, Circana, panel data, retail measurement data, or similar datasets.
Experience with Snowpark, particularly Python, for complex transformation logic beyond standard SQL.
Experience with Azure Data Factory, Azure Storage Accounts, Azure Key Vault, and Azure DevOps.
Experience with CI/CD implementation for Snowflake and dbt deployments.
Experience migrating data pipelines from Databricks to Snowflake, including Delta Live Tables and Unity Catalog.
Familiarity with Snowflake RBAC, masking policies, row-access policies, and other security frameworks.
Experience with Databricks, PySpark, Delta Lake, or Azure Databricks.
Knowledge of Power BI or other business intelligence and visualization platforms.
Relevant certifications in Snowflake, dbt, Azure, or data engineering are desirable.
Continuous-improvement mindset with a strong focus on data accuracy and reliability.
Ability to build effective relationships, influence stakeholders, and collaborate across global teams.
Demonstrated ownership, attention to detail, curiosity, and commitment to delivering outstanding results.
Applicants must meet minimum age qualifications in the country in which the job is located.
