Full-Stack Data Platform Engineer

Job summary

The engineer will design, implement, and maintain a scalable data warehouse, developing and optimizing ETL pipelines to ingest data from various sources. They will also model datasets, build visualization tools, and ensure data reliability, cost efficiency, and performance across environments.

Job details

We’re building Reflow, a workforce and workflow intelligence platform that helps teams deeply understand how work gets done. As we scale, the data we collect is becoming richer and more complex. We need a data platform engineer to help us design and operate the systems that turn that data into intelligence — powering analytics, workflow insights, and economic modeling. What you’ll do Design, implement, and maintain a scalable data warehouse (BigQuery, Snowflake, Redshift, or similar). Develop and optimize ETL pipelines to ingest data from APIs and internal systems. Model and manage datasets to support flexible analytics and product features. Collaborate with engineering team to improve data mining and analytics performance. Build and maintain dashboards and visualization tools (Metabase, Tableau, Power BI) to enable internal and external insights. Ensure data reliability, cost efficiency, and performance optimization across environments. Implement event-based pipelines for real-time analytics and reporting. Contribute to data governance, privacy, and security best practices. Who you are Experienced in data warehouse architecture and scalable analytics infrastructure. Strong with SQL, data modeling, and pipeline performance optimization. Hands-on with ETL tools, data ingestion frameworks, and cloud-based data operations. Capable of balancing technical depth with real-world impact — you build systems people actually use. Comfortable navigating tradeoffs between cost, scalability, and complexity. Curious about how data translates into insights, decisions, and automation. Bonus points Experience with Python for analytics and data wrangling. Familiarity with AI/ML-driven analytics or predictive modeling. Exposure to real-time or streaming data architectures. Understanding of data governance, compliance, and secure cloud operations. Why join You’ll build the backbone of Reflow’s intelligence layer, the systems that make our data usable, fast, and insightful. You’ll work directly with founders and engineers across analytics, infrastructure, and product. This is a high-impact technical role that sits at the intersection of scale, performance, and strategy. We’re open to part or full-time. Ideal for builders who care about performance and precision at scale. Compensation: We offer competitive pay based on the market and where you’re located. The salary ranges in our job postings are intentionally wide because they need to cover both U.S. and international candidates. Our final offer will depend on things like your experience, skill set, and location.

What you’ll do

The engineer will design, implement, and maintain a scalable data warehouse, developing and optimizing ETL pipelines to ingest data from various sources. They will also model datasets, build visualization tools, and ensure data reliability, cost efficiency, and performance across environments.

Requirements

Candidates must be experienced in data warehouse architecture and scalable analytics infrastructure, demonstrating strength in SQL, data modeling, and pipeline performance optimization. Practical experience with ETL tools, data ingestion frameworks, and cloud-based data operations is required.

Listed skills

• Power BI · Preferred
• SQL · Preferred
• Technical · Preferred
• precision · Preferred
• Reliability · Preferred
• Data analysis · Preferred
• Compliance · Preferred
• Pipeline · Preferred
• Time · Preferred
• Warehouse · Preferred
• Flexible · Preferred
• efficiency · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Data Warehouse Architecture
• Scalable Analytics Infrastructure
• SQL
• Data Modeling
• Pipeline Performance Optimization
• ETL Tools
• Data Ingestion Frameworks
• Cloud-Based Data Operations
• BigQuery
• Snowflake
• Redshift
• Metabase
• Tableau
• Power BI
• Python
• Real-Time Analytics

Job areas

• Data & Analytics
• Engineering
• Software
• Technology
• Science & Research

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