Analytics Engineer

Client of HR Ways

Data Modeling & Engineering: Develop, test, and maintain high-performance dbt data pipelines within our ELT framework.

End-to-End Product Ownership: Own the lifecycle of data products from understanding the initial business question to deploying the final dashboard or automated insight.

Strategic Collaboration: Partner closely with business units (Product, Finance, Ops) to identify value-creation opportunities and translate vague requirements into technical specs.

Performance Visibility: Build and manage reports that provide "single source of truth" visibility into company performance at all levels, from executive KPIs to granular operational metrics.

Quality & Standards: Ensure data integrity and documentation across the warehouse, advocating for best practices in version control and testing.

Required Skills & Experience

Experience: 2+years of professional experience working with ELT Data Warehousing (e.g., Snowflake, BigQuery, Redshift).

The Tech Stack: Expert-level SQL and proficiency in Python. You should be comfortable with advanced transformations and automation.

DBT Mastery: Hands-on experience with dbt (data build tool) is essential. You understand how to use macros, tests, and documentation features effectively.

Fintech Context (Preferred): Experience navigating the complexities of financial data (e.g., ledgers, transaction flows, reconciliation) is a major plus.

Language: Full professional fluency in English (written and spoken).

AI-Augmented Productivity: You are proficient in leveraging LLMs (e.g., ChatGPT, Claude, GitHub Copilot) to accelerate code generation, SQL optimization, and documentation. You view AI as a pair-programmer that allows you to focus on high-level architecture and business logic rather than syntax.

Mindset: A relentless obsession with business value creation. You don't just build a table because you were asked; you build it because it moves the needle.