Principal Data Engineer at Multitude Insights

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Full Time

Start Date

Immediate

Expiry Date

29 Aug, 26

Salary

0.0

Posted On

31 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Pipeline Design, Distributed Data Systems, Data Modeling, SQL, Data Warehousing, Data Orchestration, Data Observability, SaaS Infrastructure, CI/CD, Cloud Infrastructure, Technical Leadership, Mentoring

Industry

Law Enforcement

Description

About Multitude Insights Multitude Insights builds modern tools for public safety agencies. Our flagship product, BLTN, is a real-time, AI-powered bulletin sharing platform that replaces the legacy email workflows police departments have relied on for decades. By enabling officers and analysts to easily create, search, and link bulletins across agencies, we’re turning static documents into actionable intelligence, faster, safer, and more connected. We’re a venture-backed team of engineers, public safety professionals, and veterans working closely with law enforcement customers across the country. Our mission is to give the people who keep us safe the tools they actually need. The Role We’re looking for a Principal Data Engineer to design, build, and maintain the core data infrastructure that powers BLTN and the broader Multitude platform. This isn’t just a pipeline job, you’ll be the primary technical authority on all things data, shaping the architecture that enables reliable, high-quality data access across the organization. You’ll work closely with the VP of Engineering, product, and analytics stakeholders to translate real-world data needs into robust, scalable solutions, while setting the standards and patterns that the rest of the engineering team builds on. Responsibilities • Design & Build: Architect and maintain the company’s core data infrastructure, including pipelines, warehousing, ingestion, transformation, and orchestration systems. • Own the Data Platform: Take end-to-end responsibility for the scalability, reliability, and security of the data platform ensuring high-quality data is accessible across the organization. • Set the Standard: Establish and own data engineering best practices, including data modeling, pipeline design, and observability patterns that the broader team can build on. • Champion Data Quality: Drive data quality and observability initiatives across the platform, ensuring stakeholders can trust the data they rely on. • Collaborate Cross-Functionally: Partner closely with engineering, product, and analytics teams to understand data needs and translate them into concrete technical solutions. • Lead & Mentor: Mentor engineers on data engineering principles and patterns, and contribute actively to hiring and team development. • Deploy & Operate (Bonus): Experience with CI/CD pipelines and cloud data infrastructure is a plus, but not a core requirement of the role. Qualifications • 10+ years of data engineering experience, including significant time in senior, staff, or principal-level roles. • Deep expertise in data pipeline design and distributed data systems, with a track record of owning complex production data infrastructure. • Proven experience with data modeling, ingestion, transformation, and orchestration at scale in a SaaS environment. • Strong proficiency in SQL and experience with modern data warehousing technologies. • Experience establishing data quality and observability frameworks across an organization. • Experience setting technical direction at an organizational level—not just within a single team. • Strong communication skills and the ability to make complex data architecture decisions legible to non-technical stakeholders. • Prior startup or mission-critical environment experience strongly preferred. • Bonus: Hands-on experience with CI/CD pipelines and cloud data infrastructure (e.g. AWS, Databricks) at scale. If you’re looking to lead meaningful technical work and help shape a platform used by real-world operators, we’d love to talk.

Responsibilities

Architect and maintain the core data infrastructure, including pipelines, warehousing, and orchestration systems. Establish data engineering best practices and lead the technical direction for data quality and observability across the organization.