Senior Data Platform Engineer

Pattern • Lehi, UT

11d • Onsite

About The Position

Pattern accelerates brands on global ecommerce marketplaces leveraging proprietary technology and AI. Utilizing more than 66 trillion data points, sophisticated machine learning and AI models, Pattern optimizes and automates all levers of ecommerce growth for global brands, including advertising, content management, logistics and fulfillment, pricing, forecasting and customer service. Hundreds of global brands depend on Pattern’s ecommerce acceleration platform every day to drive profitable revenue growth across 60+ global marketplaces—including Amazon, Walmart.com, Target.com, eBay, Tmall, TikTok Shop, JD, and Mercado Libre. We are looking for a high-impact Senior Data Platform Engineer to design, scale, and optimize our high-performance data infrastructure. In this role, you will build the critical systems that enable our internal teams and global partners to process and query trillions of data points with speed and reliability. If you are ready to eliminate data friction, build cutting-edge platform solutions, and thrive in a fast-paced environment, you belong at Pattern!

Requirements

• 4+ years of experience in data platform engineering, data infrastructure, or backend software development with a "Data Fanatic" mindset.
• End-to-End Problem Solving in Ambiguity: Ability to take vague or high-level requirements and drive them to a concrete, production-ready solution. You must have a track record of navigating complex technical roadblocks independently and architecting systems that solve the "whole" problem, not just the immediate ticket.
• Software Engineering: Strong proficiency in building performant, concurrent data services and platform tooling. Go is preferred, but deep expertise in Java, Python, Ruby, or Scala is accepted.
• Modern Open-Source Architecture: Deep hands-on experience designing and operating modern data stacks. You should be comfortable with solutions involving Apache Iceberg, Glue catalog, and S3 for storage; Trino, Spark, and ClickHouse for compute; and Kafka for streaming, specifically within a Kubernetes environment.
• SRE Mindset & Infrastructure as Code: Apply DevOps and Site Reliability Engineering principles to data infrastructure. You must be proficient in defining infrastructure using Terraform or CloudFormation, creating self-healing systems, and setting up observability solutions (Prometheus, Grafana, or Datadog) to ensure platform reliability and scalability.
• Database & Storage Optimization: Proven ability to manage and tune relational stores (Postgres/RDS) alongside distributed systems, ensuring data consistency, efficient indexing, and optimal query performance across the platform.
• AI-Native Platform Thinking: Comfort building a platform that both humans and AI agents consume. You should be able to expose data and infrastructure through well-documented, programmatic interfaces (catalogs, metadata, semantic layers, MCP servers, and agent-safe query paths), and you actively use AI coding tools to increase your own throughput.
• Opinionated Collaboration & Communication: A strong technical voice who thrives in design reviews. You must be able to articulate complex architectural trade-offs, defend your design choices with data, and professionally challenge the status quo when you see a better way to solve a business problem.

Nice To Haves

• Production Go experience
• Spark-on-Kubernetes operations
• CDC-based ingestion (Debezium or similar)
• data catalog and lineage tooling
• Snowflake or Databricks depth
• meaningful open-source data tooling contributions

Responsibilities

• Design, build, and operate scalable, high-performance data infrastructure supporting both batch and real-time processing on a modern open-source stack: Iceberg, Spark, Kafka, etc.
• Take a vague or high-level problem statement, drive it to a concrete architecture, and ship it to production end to end.
• Define and manage platform infrastructure as code, and build the self-healing, observable systems that keep it reliable without manual intervention.
• Build the tooling, SDKs, and self-service paths that let Data Engineers, Software Engineers, and AI agents use the platform safely without asking for help.
• Implement optimized data pipeline architectures and data access patterns that eliminate friction across global teams.
• Collaborate cross-functionally with Software Engineers, Data Engineers, and Product teams to meet evolving technical requirements, and bring a strong point of view to design reviews.
• Integrate cloud security and access control best practices across all distributed data assets.
• Troubleshoot, debug, and resolve complex performance bottlenecks in distributed data systems to ensure platform uptime and data integrity.
• Build diagnostic and monitoring tools to proactively identify and resolve issues affecting system health and data availability.

Benefits

• Unlimited PTO
• Paid Holidays
• Onsite Fitness Center
• Company Paid Life Insurance
• Casual Dress Code
• Competitive Pay
• Health, Vision, and Dental Insurance
• 401(k) match. Pattern matches 100% of the first 3% in eligible compensation deferred and 50% of the next 2% in eligible compensation deferred.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed