Principal Data Engineer Edit listing

Vaco is supporting an Arizona based client to hire a principal level Data Engineer who will lead the modernization and optimization of the foundational data ecosystem. In this role, you will design and operate scalable, resilient infrastructure to power distributed analytics, machine learning, and AI-driven workflows. You are a builder at heart---responsible for writing proof-of-concepts, defining coding standards, and championing AI-assisted development tools that reduce toil and accelerate team velocity. By partnering with other principal engineers and mentoring the broader team, you will bridge the gap between high-level architecture and seamless execution to deliver a world-class, self-service data experience for the entire organization. We are moving fast and scheduling interviews this week. Please reach out to the Vaco team today to learn more! **We are not able to work with 3rd parties or provide sponsorship for this role.**

**Core Qualifications:**

* 15+ years of software engineering experience, with a minimum of 10 years specializing in backend distributed systems or data infrastructure at scale.
* Expert-level Python and SQL proficiency (10+ years) rooted in a career of building production-grade software.
* Hands-on mastery of Snowflake (internals and clustering), dbt (macro design and Jinja), Airflow (scheduler internals), and Power BI (Import vs. Live connection).
* Comprehensive knowledge of AWS services, including IAM, VPC, Glue, S3, SFTP, Lambda, CloudWatch, and SNS.
* Proven track record of designing large-scale data platforms with a deep understanding of CAP theorem, eventual consistency, and the trade-offs between batch and streaming architectures.
* Experience implementing Gitlab CI/CD and DataDog for robust system monitoring and alerting.
* Ability to design RAG architectures, manage vector databases, and integrate LLMs into complex data pipelines.
* Skilled in writing persuasive RFCs and ADRs that drive consensus among architects and engineering leadership.
* Proven ability to influence technical strategy and facilitate cross-functional alignment across organizational levels without direct managerial authority.
* Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.

**Position Responsibilities:**
*Technical Strategy & Platform Architecture*

* Define the long-term technical architecture for the Enterprise Data Platform, translating business strategy into scalable Data Mesh and domain-oriented specifications.
* Implement automated CI/CD pipelines and Infrastructure as Code (IaC) to foster a unified engineering culture across disciplines.
* Design robust APIs that enable seamless data consumption across operations, finance, and product teams.

*Data Engineering at Scale*

* Build resilient ELT/ETL pipelines utilizing S3, Lambda, Glue, dbt, and Airflow (MWAA).
* Establish data quality, observability, lineage, and SLAs as core, first-class features of the data platform.
* Standardize enterprise-wide schema design, modeling patterns, and deployment workflows.

*AI-Assisted Development & Integration*

* Lead the integration of AI-assisted development (Cursor, MCP, Copilot) to accelerate developer velocity and reduce cognitive load.
* Scale LLM-driven code generation for AWS data pipelines, including automated test creation, documentation, and semantic schema generation.
* Leverage Amazon Bedrock, Snowflake Cortex, and AI-enabled IDEs to optimize the data lifecycle and reduce delivery lead times.

*Engineering Standards & Technical Influence*

* Enforce high standards for code quality through rigorous PR reviews, unit testing, and automated schema validation within CI/CD pipelines.
* Architect SRE-focused resiliency frameworks and self-healing systems to ensure 99.9% availability for critical data pipelines.
* Partner with Data Science, Product, and Platform teams to align on performance, compliance, and feature engineering.
* Mentor Senior and Lead engineers through design reviews, RFCs, and pair programming to elevate the organization's technical bar.

*Machine Learning & Infrastructure*

* Design and productionize end-to-end ML infrastructure, including feature stores, model experimentation frameworks, and deployment monitoring.
* Build optimized ETL/ELT workflows for training data and model deployment leveraging Snowflake ML (Snowpark) and Amazon SageMaker.

*Semantic Engineering & Graph RAG*

* Lead the design and POC of Graph RAG (Retrieval-Augmented Generation) architectures, enabling LLM agents to query structured Snowflake data via Knowledge Graphs.
* Apply knowledge of open-source Knowledge Graph solutions to create advanced patterns for semantic data retrieval.
* Coordinate project testing, deployment, and post-implementation activities, ensuring successful project delivery and that lessons learned are applied to future projects.

Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual's skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company's 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products.