Climb is a Data and AI consultancy that partners with enterprises to design, build, and operationalize modern data platforms and production AI systems. As a Databricks partner, we go deep on lakehouse architecture, machine learning, and applied AI, with a bias toward production over proof of concept. Our team brings deep technical expertise and a builder's mindset to every engagement, and we measure our work not just by what ships, but by the business impact it drives.

Role Summary

Senior Data Engineers own complete data engineering workstreams within client engagements. You design, build, and operationalize the data platforms that power analytics and AI, taking your work from discovery and design through implementation, production, and handoff.

This is a hands-on engineering role built around ownership. You work directly with client stakeholders to translate business requirements into scalable data solutions, making the technical decisions necessary to deliver reliable, secure, and cost-efficient platforms. While the Data Architect owns the overall platform architecture, you own the successful delivery of your workstreams and the quality of the systems you build.

Our work focuses on modernizing enterprise data platforms using Databricks and the broader cloud ecosystem, enabling organizations to build trustworthy data products and AI-ready foundations that last beyond the engagement.

Key Responsibilities

1. Own one or more data engineering workstreams, from technical design through production deployment and handoff.
2. Design and implement scalable data models and lakehouse architectures, including medallion patterns where appropriate.
3. Optimize performance and cost across Databricks and the underlying cloud: you treat compute spend as your problem, not someone else's.
4. Orchestrate workflows using Databricks Workflows, Delta Live Tables, or equivalent tooling.
5. Implement governance, security, observability, and lineage with Unity Catalog, and stand up CI/CD for data.
6. Design and build AI-ready data platforms that enable reliable analytics, machine learning, and agentic applications.
7. Work directly with client stakeholders to gather requirements and translate them into technical solutions.
8. Review code, mentor junior engineers, and maintain high engineering standards across your workstreams.
9. Contribute reusable accelerators, frameworks, and best practices back to the practice.

Required Qualifications

1. 6+ years in data engineering or analytics engineering.
2. Advanced experience building production data pipelines with Apache Spark (PySpark and SQL), including performance optimization.
3. Hands-on production experience with Databricks (Delta Lake, Jobs, Workflows, Unity Catalog).
4. Deep experience with at least one major cloud (AWS, Azure, or GCP).
5. Strong SQL and data modeling fundamentals.
6. A track record of building and operating production-grade data pipelines, not just prototypes.
7. Comfortable working directly with client stakeholders to translate business requirements into technical solutions.
8. Demonstrated ability to independently own a data engineering workstream from design through production.

Preferred Qualifications

1. Experience with Delta Live Tables, structured streaming, or real-time pipelines.
2. Familiarity with dbt, Airflow, or other modern data stack tooling.
3. A demonstrated performance- and cost-optimization mindset (cluster sizing, Photon, file layout, partitioning).
4. Exposure to governed or regulated environments (e.g., financial services, healthcare).
5. Prior consulting, systems integrator, or professional services experience.

Note on certification: Existing Databricks certifications are a plus. Where not already held, Databricks certification (e.g., Data Engineer Associate/Professional) is expected to be obtained post-hire.

Who Thrives Here

This role is designed for engineers who are ready to own complete workstreams today and are growing toward whole-system architecture and engagement leadership.

1. You naturally take ownership of complete workstreams rather than waiting for individual tasks.
2. You treat data quality, reliability, and performance as product features, not afterthoughts.
3. You enjoy solving ambiguous problems with practical engineering.
4. You leave every platform easier to operate than when you found it.
5. You'd rather ship something maintainable than demo something clever.

Why Climb

1. Ground floor, real backing. You are joining early, with founders who have built and exited firms like this before. You help write the playbook rather than inherit one.
2. Outcomes, not hours. We sell and deliver against business results. Advancement is tied to delivery performance and account impact, not utilization targets.
3. Senior team, no body-shop drag. Small pods of A-players, heavy internal AI leverage, and no bloated middle layers between you and the work.
4. IP that compounds. Every engagement feeds reusable accelerators, patterns, and points of view back into the practice.

What We Offer

1. Competitive base salary with performance-based bonuses
2. MacBook Pro and swag kit so you can do your best work
3. Comprehensive health, dental, and vision insurance
4. Generous holidays, flexible PTO, and remote-first work environment
5. Professional development budget including Databricks and cloud certifications
6. Spot bonuses for relevant certifications
7. Conference attendance and thought leadership opportunities
8. Collaborative, low-ego culture with direct access to leadership
9. Opportunity to shape a growing practice from the ground floor

Climb is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

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