Senior Data Platform Engineer

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

Job summary

Jobtailor seeks a senior Data Platform Engineer to design, build, and operate scalable data infrastructure for both batch and real-time processing. You will translate vague requirements into concrete architectures and production deployments, while driving infrastructure as code and robust observability across distributed systems.

The role emphasizes Go, Spark, and modern data stacks (Iceberg, Glue, S3, Trino) with a focus on performance, reliability, and security.

Qualifications

• 4+ years of experience in data platform engineering, data infrastructure, or backend software development.
• Ability to take vague or high-level requirements and drive them to production-ready solutions.
• Strong proficiency in performant, concurrent data services and platform tooling.

Responsibilities

• Design, build, and operate scalable, high-performance data infrastructure for batch and real-time processing.
• Define and manage platform infrastructure as code.
• Build self-healing and observable systems requiring minimal manual intervention.
• Integrate cloud security and access-control best practices across distributed data assets.
• Establish CI/CD pipelines and proactive alerting mechanisms.

Skills

Data Platform Engineering

Go Programming

Apache Spark

Terraform

Kubernetes

Tools

Apache Iceberg

AWS Glue

Amazon S3

Trino

ClickHouse

Kafka

Prometheus

Grafana

Datadog

Postgres

Job description

• Design, build, and operate scalable, high-performance data infrastructure for batch and real-time processing
• Drive vague or high-level problem statements to concrete architectures and production deployments end to end
• Define and manage platform infrastructure as code
• Build self-healing and observable systems requiring minimal manual intervention
• Build tooling, SDKs, and self-service paths for Data Engineers, Software Engineers, and AI agents
• Implement optimized data pipeline architectures and data access patterns for global teams
• Collaborate with Software Engineers, Data Engineers, Product, Marketing, and other departments
• Participate in design reviews and provide technical direction
• Integrate cloud security and access-control best practices across distributed data assets
• Troubleshoot and resolve performance bottlenecks in distributed data systems
• Build diagnostic and monitoring tools to identify system-health and data-availability issues
• Optimize cloud costs and infrastructure performance
• Maintain and improve platform components ingesting and querying tens of trillions of data points
• Participate in peer code reviews and technical documentation
• Establish CI/CD pipelines and proactive alerting mechanisms
• Report directly to the VP of Data and Architecture
• Independently design and execute platform features and contribute to operational processes

Requirements

• 4+ years of experience in data platform engineering, data infrastructure, or backend software development
• Ability to take vague or high-level requirements and drive them to concrete, production-ready solutions
• Strong proficiency in performant, concurrent data services and platform tooling
• Deep expertise in Go, Java, Python, Ruby, or Scala; Go preferred
• Hands-on experience designing and operating modern data stacks involving Apache Iceberg, Glue catalog, S3, Trino, Spark, ClickHouse, and Kafka
• Experience operating data stacks within Kubernetes
• Proficiency with DevOps and Site Reliability Engineering principles
• Proficiency defining infrastructure with Terraform or CloudFormation
• Experience creating self-healing systems and observability solutions using Prometheus, Grafana, or Datadog
• Ability to manage and tune Postgres/RDS and distributed systems
• Knowledge of data consistency, efficient indexing, and query performance optimization
• Ability to expose data and infrastructure through catalogs, metadata, semantic layers, MCP servers, and agent-safe query paths
• Experience using AI coding tools
• Ability to articulate architectural trade-offs and defend design choices with data
• Ability to professionally challenge the status quo
• Production Go experience, Spark-on-Kubernetes operations, CDC-based ingestion, data catalog and lineage tooling, Snowflake or Databricks, and open-source data tooling contributions are strong nice-to-haves

Core Competencies

Demonstrates expertise in designing and operating scalable data infrastructure, with a strong focus on performance optimization, observability, and self-healing systems. Proficient in leveraging modern data stacks and cloud technologies to deliver production-ready solutions.

Highest-signal resume keywords

• Data Platform Engineering
• Go Programming
• Apache Spark
• Terraform
• Kubernetes

Hard Skills

• Data Infrastructure
• Backend Software Development
• Concurrent Data Services
• Performance Optimization
• Data Consistency
• Query Performance
• Self-Healing Systems
• Observability Solutions
• Data Cataloging
• AI Coding Tools

Soft Skills

• Problem Solving
• Collaboration
• Technical Direction
• Challenging Status Quo
• Articulating Architectural Trade-offs

Industry Keywords

• DevOps
• Site Reliability Engineering
• Infrastructure as Code
• CI/CD Pipelines
• Distributed Systems

Tools & Technologies

• Apache Iceberg
• AWS Glue
• Amazon S3
• Trino
• ClickHouse
• Kafka
• Prometheus
• Grafana
• Datadog
• Postgres