Staff Data Platform Engineer

BetterUp5 days ago

Location

All Hubs

Type

Full Time

Salary

USD 194,400 – 270,000

Level

Staff

Role

Staff Data Platform Engineer

Posted

Jul 20, 2026

Full TimeStaff

The role

Summary

As a Staff Data Platform Engineer at BetterUp, you'll architect and maintain enterprise-grade data systems that power AI-driven product innovation and analytics capabilities. This role combines DataOps, MLOps, and infrastructure expertise to enable both analytics and AI platform functionality at scale. You'll work with cross-functional stakeholders to implement centralized data standardization, security, and governance practices while mentoring engineers across the organization on data systems architecture.

What you'll do

Architect and Maintain Scalable Data Systems: Design, build, and maintain enterprise-grade data systems that deliver measurable customer value and support BetterUp's data products. Ensure systems are optimized for performance, reliability, and scalability across the global platform.

Standardize Data Infrastructure and Processes: Centralize and standardize data collection, transformation, analysis, storage, and serving approaches across the organization. Implement consistent tooling and processes that enable operational efficiency and reduce technical debt.

Enable ML and GenAI Capabilities: Provide platform capabilities for data delivery, processing, and infrastructure that support machine learning and generative AI teams. Design systems that facilitate feature engineering at scale, vector search, embeddings, and MLOps tooling integration.

Support Analytics and Reporting Teams: Enable internal and customer-facing analytics teams to deliver business insights through robust data pipeline architecture. Maintain and optimize reporting infrastructure and data serving capabilities for both operational and strategic decision-making.

Set Technical Direction and Strategy: Define technical roadmap and direction for data platform evolution over a 1-2 year horizon. Establish standards and best practices for data infrastructure, tooling, and AI-augmented workflows that other engineering teams adopt across the organization.

Mentor and Influence Engineering Teams: Mentor engineers across the organization on data systems architecture, distributed systems design, and AI-augmented engineering practices. Influence technical decisions and help establish organizational standards for data platform development.

Evaluate and Adopt Emerging Technologies: Stay current with emerging technologies and trends in the evolving data and AI ecosystem. Evaluate new tools and platforms, drive adoption of the highest-impact solutions, and ensure the organization remains at the forefront of data engineering practices.

Manage Data Infrastructure Operations: Operationally manage data infrastructure, vendors, tooling, and processes. Maintain CI/CD systems, Infrastructure as Code practices, Kubernetes deployments, monitoring, security, and privacy best practices to ensure production reliability.

What we look for

Technical

Strong Python ProgrammingProficient in Python with demonstrated ability to build production-grade data systems, automation scripts, and infrastructure code. Experience writing clean, maintainable code that can be deployed at scale.

Data Platform ArchitectureDeep expertise in designing end-to-end data systems using tools like Apache Airflow, Kafka, and related orchestration and streaming technologies. Understanding of distributed systems design, data pipeline patterns, and scalability considerations.

Analytics Stack ImplementationHands-on experience building and maintaining analytics stacks including Snowflake, dbt (data build tool), and business intelligence tools like Looker. Expertise in data warehousing, ELT/ETL patterns, and analytics data modeling.

MLOps and Production ML SystemsExperience running machine learning and generative AI systems in production environments. Proficiency with MLOps tooling, feature stores, vector databases, embedding stores, and the infrastructure required to support AI-driven applications at scale.

Operational Maturity and DevOpsAdvanced knowledge of CI/CD systems, Infrastructure as Code principles, Kubernetes orchestration, distributed systems, monitoring and observability, security hardening, and privacy best practices for data systems.

Data Governance and SecurityUnderstanding of enterprise-grade data governance, security, and privacy requirements. Experience implementing data access controls, compliance frameworks, and best practices for handling sensitive information at scale.

Education

Bachelor's Degree in Computer Science or Related FieldPreferred educational foundation in Computer Science, Software Engineering, Mathematics, or a related technical discipline, though equivalent professional experience is valued.

Experience

7+ Years in Data Platform EngineeringMinimum seven years of professional experience in data platform engineering, data infrastructure, or closely related technical roles. This should include hands-on system design and implementation at significant scale.

Cross-functional CollaborationProven ability to work effectively with diverse teams including data analysts, machine learning engineers, product managers, and infrastructure specialists. Experience translating technical concepts for non-technical stakeholders.

Leadership and MentorshipPrior experience mentoring junior engineers, establishing technical standards, and influencing architectural decisions across multiple teams. Demonstrated ability to drive adoption of best practices and technologies.

Skills

Required skills

PythonCore programming language for data systems development, automation, and infrastructure-as-code scripting with production-grade reliability requirements.

Apache AirflowExpertise in designing and managing data orchestration workflows, DAG management, and scheduling at scale for enterprise data pipelines.

SnowflakeDeep experience with cloud data warehouse architecture, performance optimization, and implementation of scalable analytics infrastructure.

dbt (Data Build Tool)Proficiency in building maintainable, modular data transformation logic and managing data lineage within analytics stacks.

KubernetesStrong knowledge of container orchestration, deployment strategies, and operational management of Kubernetes clusters for data infrastructure.

CI/CD and Infrastructure as CodeExperience implementing continuous integration and deployment pipelines, managing infrastructure through code (Terraform, CloudFormation, etc.), and automating operational workflows.

Distributed Systems DesignUnderstanding of distributed computing principles, data consistency, fault tolerance, and scalable architecture patterns for large-scale systems.

Nice to have

Apache KafkaExperience with event streaming platforms and real-time data processing architectures for building responsive data systems.

LookerKnowledge of modern BI tools and their integration with data platforms for delivering business intelligence and analytics capabilities.

Vector Databases and EmbeddingsHands-on experience with vector search, embedding stores, and the infrastructure supporting AI and machine learning model deployment.

MLOps ToolingFamiliarity with model training, serving, and management tools such as MLflow, Kubeflow, or similar platforms for production ML systems.

AWS or Google Cloud PlatformExperience with cloud infrastructure services and data warehousing solutions offered by major cloud providers.

Observability and MonitoringExpertise in implementing monitoring, alerting, and observability solutions for data systems, including tools like Datadog, Prometheus, or similar platforms.

Data Governance ToolsExperience with data cataloging, lineage tracking, and governance platforms that enable enterprise data management and compliance.

Compensation & benefits

Salary

USD 194,400 – 270,000 (annual)

Stock options

Available

Benefits

Personal BetterUp Coaching

Access to BetterUp's professional coaching platform for your own development, plus one coaching subscription for a friend or family member. This personalized coaching supports continuous learning and professional growth.

Comprehensive Health Coverage

Medical, dental, and vision insurance designed to support your overall wellness and health needs while working at BetterUp.

Flexible Paid Time Off

Flexible vacation and paid time off policy enabling work-life balance and personal wellness without restrictive accrual limits.

BetterUp Inner Workdays

Four paid days per year dedicated to personal reflection, learning, and inner work. These days support mental fitness and personal development as part of BetterUp's culture.

Volunteer Days

Five paid volunteer days annually to support causes that matter to you and give back to your community while representing BetterUp.

Learning and Development Stipend

Annual budget for professional development, certifications, courses, and conferences to support continued growth in data engineering and emerging technologies.

Company Breaks

Company-wide summer and winter break periods that align the entire organization around rest, reflection, and recharge time.

Charitable Contribution Program

BetterUp makes year-round charitable contributions to the organization of your choice, enabling you to support causes aligned with your values.

401(k) Self-Contribution

Access to 401(k) retirement savings plan enabling long-term financial planning and tax-advantaged retirement contributions.

Equity Compensation

Stock options or equity grants representing ownership in BetterUp, aligning your success with the company's long-term growth and mission.

Hybrid Work Flexibility

Flexible work arrangement with hybrid options: 2 days/week in-office for hub-based employees or fully remote with periodic travel for those outside hub locations.

Interview process

1
Application Review — Your application will be reviewed by the hiring team to assess alignment with the Staff Data Platform Engineer role and required technical qualifications.
2
Initial Screening Call — A conversation with a member of the recruiting team to discuss your background, experience in data platform engineering, and interest in the Staff Engineer role at BetterUp.
3
Technical Skills Assessment — A technical evaluation focusing on data systems architecture, platform design, and your experience with tools like Airflow, Snowflake, dbt, and distributed systems. This may include discussions of past projects and architectural decisions.
4
System Design Interview — Deep-dive conversation on designing scalable data platforms, demonstrating your ability to architect systems that handle complex requirements including analytics, ML/GenAI support, and governance at enterprise scale.
5
Leadership and Mentorship Discussion — Interview with senior engineering leadership exploring your experience mentoring teams, establishing technical standards, influencing architectural decisions, and driving adoption of best practices across organizations.
6
AI-Augmented Engineering Showcase — During interviews, you'll have opportunities to demonstrate how you leverage AI tools to learn, iterate, and amplify your impact as an engineer. BetterUp values thoughtful integration of AI into engineering workflows.
7
Manager and Team Fit Conversations — Meeting with the direct manager and potentially data platform team members to ensure alignment on working style, collaboration approach, and shared commitment to data-driven decision making and product innovation.
8
Final Offer Discussion — Conversation addressing compensation, equity, benefits, work arrangement, and the broader opportunity to help BetterUp scale its data platform while mentoring engineering teams.