Remote | Fractal

The data infrastructure behind a payments platform is either an asset or a liability. There is no in between. When it works, it powers smart decisions, accurate reporting, and product features that actually move the business. When it does not, every team feels it.

Fractal is processing real transactions for real industrial businesses. As we scale, we need a data foundation that can keep up and a senior engineer who can build it the right way from the start.

WHAT THIS ROLE IS

This is not a data engineer who only writes pipelines. It is not a role where you wait for someone to hand you specs.

You will be both a hands-on technical expert and a trusted technical voice on the team. You will drive the design of core data models and pipelines in our Snowflake and BigQuery environments, and you will set the standards for quality, reliability, and scalability across the platform.

You will own end-to-end solutions, from architecture and implementation to operations and optimization. You will collaborate across teams to turn complex business problems into robust data solutions, and you will work with engineers across the environments to make sure we follow best practices from source to outcome.

What You'll Do

• Design and own canonical analytical data models, defining grain, keys, and relationships that power our enterprise metrics and reporting.

• Build and orchestrate a layered (raw - conformed - curated) analytics platform that transforms data from diverse source systems into trusted, business-ready datasets.

• Design and evolve scalable analytical data structures and transformation tooling

• Mentor engineers and foster a culture of ownership, operational excellence, and continuous learning.

• Rationalize and evolve existing data structures into a cohesive analytical foundation, reconciling differences across source systems to deliver a single source of truth.

• Influence the long-term technical vision and roadmap for the data platform.

Required Skills

• 5+ years of experience in data engineering, with a strong background in data architecture, data modeling, and distributed data systems.

• Expertise in BigQuery, Snowflake, and relational databases (MySQL, Postgres).

• Demonstrated experience in AI implementation and data platforms (e.g., Databricks), including real work experience of AI data tooling modeling.

• Advanced SQL expertise required including complex joins, aggregations, window functions, CTEs, query optimization, and reasoning about data at different levels of aggregation.

• Experience designing dimensional and analytical data models and owning metrics across domains (analytics, ML, APIs).

• Proven success with real-world data AI deployment examples, such as implementing predictive risk, fraud detection (e.g., using Oscilar or Sardine data), or profit leakage detection systems.

• Exposure to key AI tools for model development, deployment, and governance (e.g., H2O.ai, Vertex AI, Databricks, or experience with LLM orchestration)

• Hands-on experience with SQL-based transformation frameworks and orchestration tooling.

• Demonstrated ability to lead technical initiatives, influence the standards, and decisions across teams.

• Comfort owning systems end-to-end, including monitoring, reliability, and cost management.

• Excellent communication skills with the ability to translate technical trade-offs to both engineers and non-technical stakeholders.

WHY FRACTAL

This industry has been underserved by technology for decades. The companies that figure out how to modernize it will own a significant market for a long time.

We already have product market fit. We have real customers, real revenue, and a platform processing real transactions. What we are building now is the data foundation that lets us scale that confidently and turn transaction data into a real product advantage.

You'll have direct access to leadership, real ownership over the systems you design, and the opportunity to shape how a high-growth payments platform makes decisions with data.