Principal Analytics Engineer

Curaleaf•Stamford, CT

20h•Onsite

About The Position

As the Principal Data & Analytics Engineer, you will serve as the senior technical leader for Curaleaf’s modern data and analytics ecosystem. You will define and evolve the architecture, engineering standards and shared platform capabilities that enable trusted, scalable and reusable data products across the organization. This is a hands-on individual-contributor and leadership role. You will work across Data Engineering, Analytics Engineering, Business Intelligence, Data Science, Data Governance, and business-facing product teams. You will help connect Curaleaf’s data lifecycle from source ingestion through transformation, semantic delivery, advanced analytics and business consumption. You will partner closely with the Director of Data & Analytics, Senior Data Engineer, Senior Analytics Engineer and business product owners. Success will come not only from the solutions you build, but also from the technical clarity, reusable capabilities and engineering leverage you create for the entire team. This Principal engineering role will lead through technical direction, architecture, mentoring and cross-team influence rather than direct people management. Impact will be measured across systems and teams, not solely through individual code output.

Requirements

8+ years of experience in Data Engineering, Analytics Engineering, Data Architecture, Data Platforms, or a closely related discipline
Demonstrated experience operating as a senior technical lead, staff engineer, principal engineer, architect, or equivalent individual contributor with influence across teams
Strong hands-on expertise with Snowflake, including data architecture, performance, security, workload management, access patterns, and cost optimization
Advanced proficiency in SQL and strong experience with Python for data engineering, automation, platform tooling, or analytical workflows
Significant experience with dbt or a comparable SQL-first transformation framework, including project architecture, testing, documentation, deployment, and reusable modeling patterns
Experience designing modern analytical data models, including staging layers, conformed dimensions, facts, curated marts, semantic models, and reusable data products
Strong knowledge of ingestion, ELT, orchestration, data contracts, schema evolution, data quality, lineage, and production-support practices
Experience with Git, automated testing, code review, CI/CD, environment management, and software-engineering practices applied to data systems
Experience defining architecture and technical standards while remaining sufficiently hands-on to build prototypes, solve complex problems, and guide implementation
Strong written and verbal communication skills, including the ability to explain technical tradeoffs to business and executive audiences
Demonstrated ability to mentor engineers and drive technical alignment without relying on formal people-management authority
Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent practical experience

Nice To Haves

Experience with dltHub or another modern ingestion framework.
Experience with Omni, Looker, or another governed semantic and business-intelligence platform.

Responsibilities

Define and evolve the end-to-end architecture across source ingestion, Snowflake, dbt, Omni, Hex, and related data services
Translate business strategy and product needs into scalable technical roadmaps, architectural patterns, and platform capabilities
Establish clear technology boundaries and guide where ingestion, transformation, semantic, reporting, and advanced analytical logic should reside
Architect secure, scalable, reliable, and cost-effective Snowflake environments supporting data products, BI, self-service analytics, and data science
Partner with the Senior Data Engineer on workload management, access patterns, observability, performance, resource utilization, and production support
Lead technical strategies for environment management, data access, workload isolation, platform resilience, and ongoing optimization
Define standards for SQL, Python, dbt, ingestion pipelines, data modeling, testing, documentation, deployment, and version control
Advance Git-based workflows, automated testing, CI/CD, release management, and engineering quality across the data lifecycle
Partner with business product owners, data stewards, Analytics Engineers, Data Engineers, and Data Scientists to design trusted and reusable data products
Lead root-cause reviews for significant incidents and convert recurring issues into durable platform or engineering improvements
Partner with data owners and stewards to implement quality expectations, ownership, lineage, classification, access, and appropriate-use requirements
Communicate architecture, risk, tradeoffs, and investment needs clearly to technical teams, business stakeholders, and executive leadership