Senior Data Platform Engineer

Pattern; Lehi (UT); On-site; USD 140,000 - 190,000; Full time

Job summary

Pattern seeks a Senior Data Platform Engineer to design, scale, and optimize a high-performance data infrastructure. You will work with a modern open-source stack (Iceberg, Spark, Kafka) and build scalable, self-service tooling for engineers and AI agents.

The role emphasizes end-to-end problem solving, strong collaboration, and a focus on reliability, observability, and security across distributed data assets. Utah-based, on-site with Pattern’s global tech organization.

Qualifications

• 4+ years of experience in data platform engineering, data infrastructure, or backend software development with a data-centric mindset.
• Ability to design and operate modern data stacks and scale high-performance data infrastructure.
• Experience with open-source data tools (Iceberg, Spark, Kafka) in Kubernetes environments.

Responsibilities

• Design, scale, and operate scalable data infrastructure for batch and real-time processing.
• Define infrastructure as code and build self-healing, observable systems.
• Build tooling and self-service paths for engineers and AI agents to access the platform safely.
• Collaborate cross-functionally to meet evolving technical requirements and review designs.
• Implement secure data access patterns and cloud security practices across data assets.

Skills

Go

Java

Python

Kubernetes

Terraform

Prometheus

Grafana

Datadog

Kafka

Apache Iceberg

Tools

Spark

Kafka

Apache Iceberg

Trino

ClickHouse

S3

Glue catalog

Job description

Are you obsessed with data, partner success, taking action, and changing the game? If you have a whole lot of hustle and a touch of nerd, come work with Pattern! We want you to use your skills to push one of the fastest-growing companies headquartered in the US to the top of the list.

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. To learn more, visit pattern.com or email [e-mail].

Pattern has been named one of the fastest growing tech companies headquartered in North America by Deloitte and one of best-led companies by Inc. We place employee experience at the center of our business model and have been recognized as one of Newsweek's Global Most Loved Workplaces®.

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!

What is a day in the life of a Senior Data Platform Engineer?

• 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.

What will I need to thrive in this role?

• 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.

What does high performance look like?

• Execution & Innovation: You consistently deliver efficient platform enhancements, optimize cloud costs, and increase overall infrastructure performance.

• System Scalability: You successfully maintain and improve platform components capable of ingesting and querying tens of trillions of data points.

• A Team of Doers: You actively participate in peer code reviews, contribute to technical documentation, and elevate engineering standards across the team.

• Operational Excellence: You reduce system downtime and latency by establishing robust CI/CD pipelines and proactive alerting mechanisms.

What is my potential for career growth?

• Mentorship opportunities from engineering leadership in Data and Architecture.

• Broad exposure across diverse business units within Pattern's global technology organization.

• A clear, merit-based career path with potential advancement toward Staff Engineer or Lead Data Platform roles.

• Opportunity to drive key technical initiatives that directly shape Pattern's core ecommerce acceleration platform.

• Internal promotion priority for high-performing "Pattern People."

What does success look like in the first 30, 60, 90 days?

• First 30 Days: Build context on Pattern's data stack, complete system onboarding, and begin resolving minor infrastructure issues or backlog tickets.