Analytics Engineer

Sambla Group is a fintech company with a clear goal – to be your trusted financial partner. We’ve grown into one of the fastest-growing fintech companies - bridging the gap between borrowers and lenders - thanks to the 500+ amazing people who work here.

At Sambla Group, data is central to how we make decisions, build products, and shape business strategy. We are strengthening our Data Platform team and are looking for an Analytics Engineer to help design, build, and maintain the analytical foundation used across the organization.

You will join a team of Data Engineers and Analytics Engineers, reporting to the Engineering Manager. This is a technical role focused on building scalable, trusted, and well-documented data products - not one-off reports. You will work with SQL, dbt, semantic models, testing frameworks, cloud data warehouses, and modern analytics practices to ensure that teams across Sambla can confidently use data for analysis, decision-making, and AI-enabled insights.

Key Responsibilities

Analytics Engineering & Data Modeling

• Design, build, and maintain scalable ELT models using SQL, dbt, and Git-based workflows.

• Develop robust transformation layers that turn raw data into trusted, reusable analytical assets.

• Build and own core analytical models, including dimensional models, marts, metric layers, and semantic models.

• Define and maintain standards for naming, documentation, testing, version control, and governance.

• Work closely with Data Engineering to align source data structures with downstream analytical needs.

Testing, Quality & Governance

• Implement dbt tests, source freshness checks, data validations, and business logic controls.

• Ensure strong data lineage, documentation, ownership, and discoverability of analytical assets.

• Monitor and improve data quality, reliability, and performance across the analytical stack.

• Promote consistent metric definitions and trusted data products across business domains.

AI-Enabled Analytics & Self-Service

• Structure data models and semantic layers so they can support both human analysis and AI-driven analytics.

• Explore how AI can improve analytics workflows, including data discovery, documentation, anomaly detection, and natural language interaction with data.

• Create well-documented, reusable data assets that allow analysts and business users to answer questions with confidence.

• Guide stakeholders on how to use data products effectively and translate business needs into scalable analytical solutions.

Requirements

• Bachelor’s or master’s degree in Computer Science, Software Engineering, Data, Analytics, or a related technical field.

• Several years of experience in analytics engineering, data modeling, data transformation, or analytical architecture.

• Expert-level SQL, including optimization, advanced functions, and analytical modeling patterns.

Strong hands-on experience with dbt or similar frameworks, including modeling, testing, documentation, and lineage.

• Experience working with cloud data warehouses such as BigQuery, Snowflake, Databricks, or similar platforms.

• Solid understanding of data warehousing, dimensional modeling, semantic modeling, and modern analytics architecture.

• Experience with Git-based workflows, pull requests, code reviews, and CI/CD for analytics assets.

• Strong focus on data quality, testing, maintainability, and scalable analytical design.

• Ability to translate business questions into clear, reusable, and well-modeled data products.

Nice-to-Have

• Experience designing semantic layers, metric stores, governed KPI frameworks, or reusable business logic layers.

• Experience with BI and reporting tools such as Power BI, Tableau, Looker, Qlik, or similar platforms.

• Exposure to AI-driven analytics, conversational analytics, natural language querying, or insight-generation platforms.

• Experience using AI tools to support analytics engineering workflows, such as documentation, testing, SQL development, or data discovery.

• Familiarity with orchestration, observability, and data quality tools in the modern data stack.

You’ll Thrive in This Role If

• You think in architecture, standards, and systems - not just individual datasets.

• You care deeply about data quality, traceability, and semantic consistency.

• You enjoy building scalable analytical foundations that empower others to work confidently with data.

• You are curious about how AI can improve the way organizations discover, understand, and interact with data.

• You like working closely with users to understand their needs and turn them into robust, reusable data products.

We might be a little different from most companies. Instead of defining personal values, we’ve built a shared way of working - a rhythm that guides how we collaborate, support our customers, and move forward together. We call it Drive & Jive - a shared rhythm that creates momentum through direction, collaboration, and human connection.

As part of our recruitment process, we always carry out a background check before extending a job offer.