About the role
We're looking for a Data Engineer to help build the data infrastructure that powers GoodScore's next phase of growth. At GoodScore, data is at the heart of everything we do, from understanding customer credit behavior and powering personalized experiences to building lending, risk, and AI/ML products. As we scale, we're building a modern, reliable, and scalable data platform that enables teams to move from raw data to insights and intelligent decisions faster.
In this role, you'll work at the intersection of data, engineering, product, risk, analytics, and AI/ML. You'll design and build robust data pipelines, data models, and platform capabilities that make high-quality data easily accessible across the organization. This is a high-ownership role where you'll solve complex data problems, influence our data architecture, and help establish the foundations for a truly data-driven fintech.
Responsibilities:
- Build and maintain reliable batch and near-real-time pipelines from product databases, credit bureau data, partner APIs, and event streams.
- Own the warehouse: clean modelling, naming, documentation, and cost-aware performance.
- Set up data quality checks, alerting, and SLAs so that dashboards, WBRs, and models run on numbers people trust.
- Work with analysts and data scientists to turn raw data into tables they can use directly, including features for risk and growth models.
- Work with engineering and product to get tracking and schemas right at the source.
- Handle PII and financial data with care: access controls, masking, and audit trails.
- Automate manual reporting and ad hoc data pulls.
Requirements:
- Strong engineering background with education from Tier I technology schools.
- 2 to 5 years building and running data pipelines in production.
- Strong SQL, including window functions, query tuning, and dimensional modelling.
- Strong Python for data processing and automation.
- Hands-on with a workflow orchestrator (Airflow, Dagster, or similar).
- Experience with a cloud data warehouse or lakehouse (Databricks, BigQuery, Redshift, or similar) on AWS, GCP, or Azure.
- Good habits in testing, monitoring, and debugging broken pipelines. Clear communication with non-engineers.