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.

• 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.

• 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.

• 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."

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

• First 60 Days: Take ownership of key platform components, optimize pipeline performance, and deliver meaningful reliability improvements.

• First 90 Days: Independently design and execute platform features, contribute to technical discussions, and actively participate in the team's operational rhythm.

We are looking for individuals who are:

• Game Changers- A game changer is someone who looks at problems with an open mind and shares new ideas with team members, regularly reassesses existing plans and attaches a realistic timeline to goals, makes profitable, productive, and innovative contributions, and actively pursues improvements to Pattern’s processes and outcomes.
  

• Data Fanatics- A data fanatic is someone who recognizes problems and seeks to understand them through data, draws unbiased conclusions based on data that lead to actionable solutions, and continues to track the effects of the solutions using data.

• Partner Obsessed- An individual who is partner obsessed clearly explains the status of projects to partners and relies on constructive feedback, actively listens to partner’s expectations, and delivers results that exceed them, prioritizes the needs of your partners, and takes the time to create a personable experience for those interacting with Pattern.
  

• Team of Doers- Someone who is a part of a team of doers uplifts team members and recognizes their specific contributions, takes initiative to help in any circumstance, actively contributes to supporting improvements, and holds themselves accountable to the team as well as to partners.
  

1. Initial interview with Pattern's talent acquisition team

2. Technical video interview with a senior member of the data platform team

3. Onsite/Panel interview with peers and engineering leadership

4. Professional reference checks

5. Executive review

6. Offer

• Strong 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, and meaningful open-source data tooling contributions.

• Quantify Success: During interviews, share tangible achievements using data metrics to demonstrate operational impact and system efficiency gains.

• Show Your Ambiguity Muscle: Bring an example of a problem that arrived as one sentence and left as a production system, and be ready to walk through the trade-offs you rejected along the way.

• Show Your Passion: Share side projects or personal repositories focused on data engineering, infrastructure automation, or large-scale analytics.

• The "Partner" Lens: Articulate clearly how high infrastructure reliability and data platform speed directly enable better outcomes for global ecommerce partners.