Position Description

We’re seeking a Principal Data Engineer to own data architecture across our connected equipment platform. Reporting directly to the CTO, you’ll design the data systems and patterns that our engineering teams implement, from our IoT device ingestion pipeline handling data from our growing fleet of devices, to our data storage and processing infrastructure, to the data models and APIs that power our products. This is an organization-wide architectural role: you’ll set data architecture standards, provide design oversight to engineering teams, and ensure our data infrastructure can support our aggressive growth. If you thrive at the intersection of hands-on data engineering and architectural leadership, apply today!

Why Tenna?

We take pride that our roots are in the construction industry. With this experience and knowledge, we have built a platform that revolutionizes construction equipment fleet operations. We provide innovative solutions to customers looking for competitive ways to better manage and track their assets, such as heavy and light equipment, large fleets, tools, and materials.

At Tenna, we believe the best is right in front of all of us and that each day holds more potential than the one before. We believe that every new discovery can lead to something better than we thought possible. When we boil it down, the top five qualities that define the Tenna Team are quality-obsessed, gritty, continuous learners, collaborative problem solvers, and just plain awesome. Sound like you? Join us as we empower our customers to control their mixed assets on one comprehensive platform anytime, anywhere.

Your Responsibilities

Owns organizational-wide data architecture, defining standards, patterns, and designs that our teams will implement. Solves complex data challenges regardless of perceived ambiguity or degree of clarity.
Reviews data-related designs and implementations across teams for architectural consistency, performance, and scalability.
Produces reference implementations, proofs of concept, and hands-on guidance when teams encounter complex data engineering challenges.
Designs and develops data pipelines, integrations, and platform features with performance and scalability in mind.
Builds and maintains data APIs and ingestion systems that can handle complex, high-volume data efficiently.
Takes responsibility for the quality of data systems across the organization, including testing strategies and data integrity standards.
Owns the data architecture strategy across the platform, including database design patterns, data API standards, and data flow architecture.
Consults with product managers to define, scope, and plan new data features and capabilities.
Partners with the CTO and engineering leadership on strategic data initiatives and long-term architectural direction.
Partners with teams to define data quality standards, automated data validation patterns, and testing strategies for data pipelines.
Partners with engineering and product teams to design and support data infrastructure for AI/ML initiatives, including model training pipelines, feature stores, and inference data flows.
Designs the IoT data ingestion and distribution architecture to support data from a significantly expanding device fleet, including replayability, aggregation, and real-time distribution patterns.
Tests, evaluates, and recommends technologies to improve our overall data infrastructure.
Produces excellent documentation.

Qualifications

12+ years of professional data engineering or software development experience; self-motivated and driven to deliver impactful data products.
2+ years’ experience providing architectural direction and design oversight to engineering teams, not just individual mentorship. Excellent communication skills are a must.
Experience setting data architecture standards and providing technical oversight across multiple engineering teams.
Bachelor of Science in Computer Science, Data Engineering, or equivalent experience; intimately familiar with the fundamentals of computer science, data architecture, and distributed systems.
Substantial experience with SQL; experience with NoSQL is a plus.
Experience with Python for data engineering workflows; experience with Node.js is a plus.
Experience with distributed data processing frameworks such as Apache Spark or Apache Flink is a plus.
Experience with data orchestration tools such as Apache Airflow or similar is a plus.
Experience with containerized application deployments, especially using Docker, is highly preferred.
Experience with large-scale data systems and data warehousing solutions is highly preferred; possesses in-depth knowledge of the open source data ecosystem and how to incorporate it into scalable solutions.
Experience with message queueing architectures, especially RabbitMQ, is preferred.
Experience with Amazon Web Services, especially S3, RDS, DMS, and VPC; experience with Redshift or EMR is a plus.
Experience working with AI/ML systems, including building data infrastructure to support model training, inference pipelines, or AI-powered features, is a plus.
Experience designing data architectures for IoT or high-frequency telemetry systems is highly preferred.

Benefits, Perks, & Additional Information:

Full-time opportunity.
Location: Remote - nationwide.
Travel is required, 8 - 10%.
Opportunities for growth and personal development within a highly dynamic team.
Robust, low-cost benefit packages offered.
Benefit coverage begins on the first date of employment.
Paid Time Off and Volunteer Time Off offered.
401k match.
Dependent Care offered.
Employee referral bonuses.

As an Equal Opportunity Employer, Tenna is committed to building a diverse team. We welcome different perspectives and opinions to foster innovation, authenticity, and excellence across all parts of our company, and are committed to providing employees with a work environment free of discrimination and harassment.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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