Analytics Engineer

We are seeking a skilled and detail-oriented Analytics Engineer with 1-3 years of experience to join our growing Analytics team.

The ideal candidate will bridge the gap between data engineering and business intelligence by transforming raw data into clean, reliable, well-structured, and reusable analytical datasets. The candidate will be responsible for developing data transformation pipelines, building data models, implementing data quality checks, and optimizing datasets for reporting and analytics.

This role requires strong SQL skills, experience with data transformation frameworks, an understanding of data warehousing concepts, and the ability to collaborate with Data Engineering, Business Intelligence, Product, and Client teams to deliver trusted data solutions for enterprise loyalty programs.

What You'll Do:

• Build and maintain SQL-based data transformation models to convert raw data into clean, analytics-ready datasets.
• Develop, test, and optimize data models using SQL, dbt, or similar transformation frameworks.
• Design dimensional data models, including fact tables, dimension tables, and analytical data marts.
• Integrate data from multiple sources, including transactional databases, loyalty platforms, APIs, and external systems.
• Translate business requirements into reusable datasets, standardized metrics, and analytical models.
• Define and maintain key business metrics related to loyalty performance, customer engagement, points accumulation, reward redemption, and retention.
• Implement data quality checks, validation rules, and reconciliation processes to ensure data accuracy.
• Troubleshoot data discrepancies, monitor pipeline execution, and optimize queries and transformation workflows.
• Collaborate with BI, Data Engineering, Product, and other teams to support dashboards, reporting, and business analytics.
• Maintain data documentation, model dependencies, and transformation logic while following coding, testing, and version control best practices.

What We're Looking For:

• Bachelor's degree in Computer Science, Engineering, IT, Statistics, Mathematics, or a related field.
• 1-3 years of experience in Analytics Engineering, Data Engineering, BI Development, or a similar role.
• Strong SQL skills, including complex joins, CTEs, window functions, aggregations, and query optimization.
• Hands-on experience in data transformation, data modelling, and building analytics-ready datasets.
• Understanding of data warehousing, dimensional modelling, star schemas, fact tables, and dimension tables.
• Familiarity with dbt, SQL-based ETL/ELT frameworks, and data pipeline concepts.
• Experience with relational databases, data validation, testing, and version control tools such as Git.
• Strong analytical, problem-solving, and debugging skills.
• Ability to translate business requirements into scalable data solutions and collaborate with cross-functional teams.

Good to Have:

• Experience with Snowflake, Google BigQuery, Amazon Redshift, or Databricks.
• Hands-on experience with dbt models, tests, macros, and documentation.
• Basic to intermediate Python skills for data processing and automation.
• Familiarity with Apache Airflow, Dagster, or similar orchestration tools.
• Exposure to cloud data warehouses, data lakehouse architectures, and CI/CD practices.
• Experience working with large-scale customer, transactional, or event-driven datasets.
• Familiarity with CRM, CDP, enterprise SaaS, or customer loyalty platforms.
• Understanding of loyalty concepts such as reward points, customer tiers, redemption, and customer engagement.

Key Competencies:

• SQL & Query Optimization
• Analytics Engineering & Data Transformation
• Data Modelling & Warehousing
• dbt & ELT Development
• Data Quality & Automated Testing
• Cloud Data Platforms
• Data Integration & Pipeline Development
• Documentation & Version Control
• Analytical Problem Solving
• Cross-Functional Collaboration

Key Performance Indicators (KPIs):

• Accuracy and reliability of analytical datasets.
• Quality and performance of data models and SQL queries.
• Reduction in data discrepancies and reporting inconsistencies.
• Timely availability of data for reporting and analytics.
• Effectiveness of automated testing and data quality checks.
• Reduction in manual data preparation efforts.
• Adherence to documentation and coding standards.

Skills

DWH, ELT, ETL, SQL, dbt