We made a money+ app that gives everyone high-end rewards on every payment. Fluz aims to disrupt the payment experience by making it more social. Positioned at the intersection of social media, payments, and rewards, we are a fast-moving FinTech company experiencing significant growth. We sponsor H1B work visas.

We are looking for a Data Engineer to help run and grow the data platform behind our fintech products. You will build and maintain pipelines that bring data into our warehouse and deliver it to the tools our Customer Success, Go-to-Market, Customer Messaging, and Risk teams rely on.

This is a hands-on, code-first role. You will write, test, and deploy production code, keep the platform within its SLAs, and help build the semantic layer that powers natural-language analytics at Fluz. You will be a member of the Data Engineering team and will work alongside product, engineering, support, sales, marketing, and risk stakeholders. This position will report to the Lead, Data Engineering.

What you will do

• Data platform and infrastructure
  • Manage and maintain our data stack, including Datastream replication (CDC), Airflow orchestration and monitoring, and BigQuery capacity planning and workload management
  • Own platform reliability: monitoring, alerting, incident response, and keeping pipelines within agreed SLAs for freshness, completeness, and uptime
  • Follow and improve our maintenance process: change management, version upgrades, cost and performance tuning, access control, and documentation
  • Deploy code through version control, code review, and CI/CD; write tests and data quality checks
• Pipelines from external sources
  • Build and maintain ingestion pipelines from third-party APIs, partner and bank files, and SaaS tools
  • Handle schema changes, backfills, late-arriving data, and failure recovery
  • Maintain and extend the platform tooling behind ingestion, transformation, and orchestration
• Data activation to external platforms
  • Maintain data syncs (reverse ETL) from BigQuery to the platforms our teams use for Customer Success, Go-to-Market, Customer Messaging, and Risk
  • Partner with Support, Sales, Marketing, and Product to define what data each platform needs and keep it accurate and timely
• Analytics engineering
  • Build and maintain data models (staging, intermediate, and core/mart layers) in Dataform and dbt that analysts and stakeholders can trust
  • Help build and govern a semantic layer (metric definitions, business logic, and metadata) that powers AI natural-language querying
  • Document datasets and definitions so data is discoverable and consistently understood

Qualifications

• 1–3 years of experience in data engineering, analytics engineering, or software engineering with a data focus
• Advanced SQL across OLTP and OLAP systems (PostgreSQL and BigQuery), including execution plan analysis, index design, and optimization of partitioning and clustering
• Proficiency in strictly typed Python/TypeScript for platform tooling, pipelines, scripting, and API integrations
• Hands-on experience with a cloud data warehouse (BigQuery preferred; Snowflake or Redshift also fine)
• Experience with an orchestration tool such as Airflow (or Cloud Composer, Dagster, Prefect)
• Strong software engineering fundamentals: Git workflows, code review, CI/CD, and automated testing
• Understanding of data modeling concepts (dimensional modeling, slowly changing dimensions, incremental loads)
• Working understanding of SLAs, monitoring, and incident handling for production systems
• Clear communication with non-technical stakeholders
• Ability to break down and articulate solutions to complex problems
• Knack for writing clean, readable, maintainable and reusable code
• Willingness to learn and teach
• Natural Problem Solver
• Strong sense of urgency in a professional setting

Nice-to-haves

• A bachelor’s degree in Computer Science, Software Engineering, or a related field is beneficial. Experience with Google Cloud (Datastream, Cloud Composer, Pub/Sub, Cloud Functions, IAM), Dataform or dbt with macros, reverse ETL or syncing data into CRM, customer support, or messaging platforms, and semantic layers, metrics stores, or LLM/AI-powered analytics is a plus. Familiarity with infrastructure as code (e.g., Terraform) and containers (Docker) is helpful. Experience with the agentic AI stack (building MCP servers and tools, connecting agents to data platforms, and taking agent workflows from prototype to production with evals, observability, and access controls) and experience in fintech, payments, or another regulated industry, including data governance and access control, will be highly regarded.

Our Tech Stack

Our mobile application is built with React Native and Apollo on the frontend. The app’s backend is built with Node.js, GraphQL, PostgreSQL, and Redis, deployed on GCP through CI and Docker Images. GitLab version control hosted on GCloud. We use gitlab-ci for our CI tool and Apache for load testing. Learn more at stackshare.io/fluz/fluz.

Industry

• FinTech
• Mobile Applications
• Payments
• Rewards & Incentives

EEOC

All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

About Fluz

Fluz is a collaborative earning app that maximizes your money. Get cashback on everyday spends, earn even more by growing your circle, and make and receive payments with friends. Easy to join and free from hidden fees forever, Fluz gives you so many ways to make what you have go even further.