About the Role

• The role reports to the Data Manager; overall, you will act as a bridge between data engineering and analytics, ensuring that our raw data is transformed into clean, reliable, and accessible datasets that power decisions across the business.
• In this role, you will both build scalable data models and pipelines that empower self-service analytics and develop reporting resources and dashboards when needed. You’ll work closely with the Data Manager, engineers, BI analysts, and business leaders to make sure our data is trustworthy, well-documented, and easy to use

Key Roles and Responsibilities

Data Modelling & Engineering

• Transform raw data into structured, well-documented datasets in the data warehouse.
• Design, build, and maintain scalable data models (e.g., using dbt or similar frameworks).
• Partner with data engineers to ensure data pipelines are reliable, efficient, and accurate.
• Implement data quality checks, testing, and version control for analytics workflows.

Analytics & Reporting

• Work with BI analysts and business stakeholders to design dashboards and reports that track key metrics.
• Support business units with ad hoc data needs and analysis requests.
• Synthesize complex data into insights and actionable recommendations for leadership.
• Empower end users with self-service data capabilities by building reusable datasets and clear documentation.

Strategy & Collaboration

• Collaborate with leaders across countries and functions to identify and prioritize analytics needs aligned to business goals.
• Work closely with the Commercial Analyst (BI) and Data Analyst to build a smooth analytics process flow from raw data to data-driven decisions.
• Ensure data governance best practices are followed, including metric definitions and data lineage.
• Serve as a key liaison between engineers, BI, and business teams to ensure smooth collaboration and shared understanding.

Key Qualifications & Profile

• Values: Demonstrates Turaco’s values of pushing boundaries, working with excellence, and profound respect for the individual.
• Experience: 4+ years of experience in analytics engineering, or related data roles.

Experience in start-ups or insurance/financial services/fintech is a plus.

Technical Skills:

• Strong SQL and data modelling skills (experience with dbt or similar is highly desirable).
• Hands-on experience with cloud data warehouses (Snowflake, Redshift, BigQuery, or similar).
• Proficiency in BI tools (Power BI, Tableau, Looker, or Google Data Studio).
• Familiarity with data pipeline concepts (ETL/ELT).
• Analysis Skills: Exceptional ability to extract insights from data and translate them into actionable recommendations.
• Communication Skills: Strong storytelling and visualisation abilities to present data clearly and persuasively.
• Mindset: Innovative, adaptable, and comfortable navigating ambiguity in a fast-paced environment.
• Education: Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, or related field.

What Success Looks Like

• Reliable, well-documented datasets and data models are available for analysis across teams.
• Analysts and stakeholders are empowered to self-serve most of their data needs.
• Business leaders receive timely, clear insights that guide strategic decision-making.
• Collaboration across engineering, BI, and business functions is smooth and effective.