Analytical Engineer

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Requirements:

• 4+ Years of Experience: Proven track record in building data warehouse products, designing code-based ETL processes, and delivering robust BI solutions with strong dimensional modeling skills within an analytical engineering context.
• Proficient in SQL with a passion for writing efficient, optimized queries that form the backbone of scalable, maintainable analytical systems.
• Demonstrated expertise with dbt (Core) for data modeling and transforming raw data into actionable, business-ready analytical layers.
• Familiarity with modern cloud data warehouses (e.g., Snowflake, BigQuery) and BI tools (e.g., Looker, Tableau, PowerBI, Mixpanel) to deliver comprehensive analytical solutions.
• Expertise in data quality management, including the design and implementation of robust monitoring frameworks to ensure data integrity and reliability.
• Excellent communication skills with the ability to convey complex technical and business ideas clearly and simply, making data insights accessible to both technical and non-technical stakeholders.
• Strong strategic approach to stakeholder management, ensuring that analytical engineering initiatives are aligned with overall business objectives.
• Prior experience in a Fintech B2B environment is a significant plus.
• Proficiency in SQL (we use Google Big Query) and a computing language such as Python
• At least 3 years experience working with dbt (Data Build Tool)
• Strong understanding of Git workflows and version control best practices
• Familiarity with advanced programming concepts, such as metaprogramming (Looker Liquid, dbt Jinja), is a plus
• Proficiency with the command line interface (Bash, ZSH, etc.) and ability to keep your IDE and tools in shape

Responsibilities:

• Develop & Maintain Business Logic: Create and refine dbt models that transform raw data into well-organized, business-ready tables.
• Ensure Quality & Governance: Implement comprehensive testing and maintain clear documentation for all data models.
• Seamless Integration: Structure the datamart layer to support efficient connections with our BI tools (Looker).
• Interface Between Teams: Serve as the liaison between the technical data pipelines and business-facing analytics.
• Data Architecture & Quality Assurance: Within the central data team, you will architect and optimize our analytical infrastructure to ensure complete data consistency and integrity.
• Cross-Functional Collaboration: Work closely with product managers, developers, and analysts as part of the central data team.
• Data Strategy & Product Innovation: Drive our evolving data strategy by identifying opportunities to transform data into new revenue streams.