Senior Data Engineer

Alvys

1d

About The Position

Alvys is revolutionizing the transportation logistics industry with a multi-tenant SaaS platform that streamlines freight operations. As a Senior Data Engineer, you will play a central role in designing and implementing our Snowflake-centric data architecture for the year 2026 and beyond. You'll work closely with engineering, product, and leadership teams to build a modern, scalable data platform that supports real-time and offline analytical workloads, large-scale ML and LLM model development and deployment, and LLM-based data strategy. This is a highly visible role that will directly shape the future of our data ecosystem, including the refinement of our LLM-based data strategy and enabling advanced analytics, ML-driven insights, and AI-powered products across the organization. Industry Insight Transportation logistics, a complex and fragmented domain, is ripe for technological revolution. You'll be at the forefront of automating and standardizing a sector that moves trillions of dollars' worth of goods annually, predominantly by truck, yet lacks modern tools and solutions. About Alvys Alvys is on a mission to revolutionize transportation logistics. We're evolving from a delivery organization into a product engineering organization—one that blends technical excellence with deep product context and financial accountability. Combining hands-on industry experience with a world-class technical vision, we're building a multi-tenant SaaS platform that's becoming an essential tool for transportation companies. We measure success by impact, not output. We balance innovation with sustainability—every feature we build comes with a maintenance cost we own. We think like operators, not just builders—reliability and performance are features, not afterthoughts. Our Principles Engineering Principles: Build for Real-World Impact — Our users rely on Alvys to solve real problems, not abstract ones. Every feature we ship is designed to reduce friction, increase efficiency, and drive tangible results for the people moving freight every day. Extreme Ownership and Empowerment — Ownership extends beyond building—it means being accountable for quality, reliability, and customer impact. If it's in production, it's yours—bugs, uptime, and results. Technology Debt is Strategic, Not Accidental — Technical debt is a strategic choice, not an oversight. We incur it deliberately when it unlocks speed, learning, or market advantage. Our architecture remains clean, maintainable, and scalable as we grow. Simplicity Scales; Complexity Costs — Logistics is inherently complex, but our technology shouldn't be. We prioritize simplicity in our architecture, leveraging modularity and well-defined service boundaries to reduce cognitive load. Radical Transparency — We democratize data and make it accessible across all functions to drive alignment and decision-making. Transparency also means feedback—we systematize constructive feedback loops to ensure growth. Engineering Culture: Move Fast, Learn Fast — Speed is our advantage, but we move fast intentionally. We bias for action, assume positive intent, and treat every mistake as a learning opportunity. Own the Outcome — Every engineer owns outcomes, not tickets. We take responsibility end-to-end—identifying problems, delivering solutions, and ensuring they last. Engineering-Led, Customer-Driven — Engineering leads by shaping technical strategy, but never in isolation. Designers, PMs, and Engineers share context and decisions, keeping customer outcomes at the center. Pragmatism Over Purism — Elegant code matters, but business impact matters more. We choose solutions that scale, are maintainable, and solve real customer problems—not theoretical ones. People Over Heroics — We don't reward burnout or always-on behavior. Sustainability is a feature, not a luxury. If someone needs to slow down, unplug, or take care of life outside work—that's expected.

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
• 5+ years of professional experience in Data Engineering or a related role, with a strong focus on designing and implementing data architecture.
• Strong proficiency with Snowflake (data modeling, performance optimization, SQL, security).
• Proven track record building, scaling, and maintaining data pipelines and ETL/ELT processes in a production environment.
• Experience with ML/LLM model development and deployment in production environments.
• Proficiency with SQL and one or more programming languages (Python, C#, TypeScript/Node).
• Experience with cloud-based data platforms (Azure, AWS, or GCP).
• Experience working with real-time streaming solutions and Reverse ETL workflows.
• Familiarity with machine learning pipelines, LLM-based applications, feature engineering, and model deployment.

Nice To Haves

• Experience building large-scale, multi-tenant data platforms on Snowflake.
• Experience with dbt or similar data transformation frameworks.
• Strong understanding of dimensional modeling and data warehouse design patterns (Kimball, Inmon, Data Vault, or Medallion architecture).
• Experience with Snowflake Cortex AI/ML for building LLM agents, semantic models, and AI-powered data products.
• Experience with LLM model fine-tuning, prompt engineering, and LLM-based data products.
• Experience with frontend development (React, Angular, or Vue) for building data applications and internal tools.
• Knowledge of MLOps best practices and ML orchestration tools (Airflow, Dagster, Prefect).
• Experience building data products, operational analytics, or embedded analytics.
• Experience leading or mentoring other data engineers or analytics engineers.
• Strong grasp of data governance, including compliance and security requirements.
• Background in logistics or transportation systems is a bonus.

Responsibilities

• LLM-Based Data Strategy: Define and refine Alvys' strategy for LLM-based data products, including data preparation, feature engineering, and semantic layer design for AI applications.
• ML/LLM Development & Deployment: Build and operationalize large-scale ML and LLM model development and deployment pipelines using Snowflake Cortex and modern MLOps practices.
• Snowflake Architecture & Strategy: Lead the design and evolution of our Snowflake-centric data platform, including storage, processing, semantic modeling, and integration strategies.
• Data Pipelines: Design and build reliable, performant ETL/ELT pipelines and Reverse ETL workflows for batch, streaming, and real-time data ingestion.
• Snowflake Cortex Implementation: Leverage Snowflake Cortex for AI/ML workloads, including semantic models, LLM agents, and embedded analytics.
• Analytics Enablement: Develop data models, semantic views, and transformations for both online (real-time) and offline analytical workloads, facilitating business intelligence and predictive analytics.
• Performance & Optimization: Continually monitor, optimize, and tune Snowflake data systems for high scalability, reliability, and cost efficiency.
• Collaboration & Governance: Partner with cross-functional teams to implement best practices around data security, quality, governance, and compliance.
• Thought Leadership: Serve as the organization's go-to expert for emerging data engineering trends, LLM applications, and Snowflake capabilities, providing strategic direction and mentorship to team members.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level