AI Engineer - Data

WALT; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

On-site in Santa Clara

Job summary

WALT is building the world’s first Autonomous Data Engineer – a platform of AI agents that manage data end to end. The role is an AI-native software engineer who will own the technical landscape of data products, turning raw enterprise data into trusted, deterministic answers at scale.

This hands-on position requires designing, implementing, and evaluating systems, setting technical direction for how agents reason over data, and contributing directly to core components.

Qualifications

• 10+ years building software, focused on data-intensive and analytical products.
• Proven track record as architect or principal engineer who shipped complex data platforms.
• Strong expertise in distributed systems, data modeling, and data correctness.

Responsibilities

• Own end-to-end architecture of analytical and data products (ReasonBase, inference engine, data platform).
• Design and evaluate systems for correct, reproducible, auditable analytics across large datasets.
• Make foundational decisions on data modeling, storage, and orchestration for the agent framework.
• Collaborate with engineers to translate customer problems into robust designs and scalable implementations.
• Set engineering standards for performance, reliability, security, and governance.

Skills

Data architecture

Distributed systems

SQL

Python

Java/Scala

Go

Rust

LLM/agent AI

Data-intensive analytics

Job description

WALT is building the world's first Autonomous Data Engineer - a platform of five specialized, collaborating AI agents (Ingestor, Transformer, Reasoner, Operator, and Governor) that manage an entire data platform end to end: ingestion, transformation, analytics, monitoring, and governance.

What makes WALT different is how it answers business questions. Instead of relying on an LLM to generate SQL on the fly, our Reasoner agent uses deterministic analytical inference over a Data Context Graph (ReasonBase™) - a context layer that maps business vocabulary to the underlying data. The result is the same trusted answer, every time. That reliability is why regulated, data-intensive customers in financial services, retail, and advertising trust us with their analytics: they get auditability, compliance, and consistency, not stochastic guesses.

We're an early-stage, fast-moving team obsessed with correctness, security, and delivering analytics people can actually rely on.

The Role

We're looking for an AI native software Engineer to own the technical landscape of our data and analytical products - the systems that turn raw, messy enterprise data into trusted, deterministic answers at scale.

This is a hands-on builder-and-coder role, not a purely advisory or architect one - you design, implement, and evaluate, and go deep to solve real customer problems. You'll set the technical direction for how our agents reason over data, design the platform that makes that reasoning fast, correct, and governable, and stay close enough to the code to prove out the hard parts yourself. You'll be a force multiplier for the engineering team and a key voice in how WALT scales.

If you've spent your career building analytical and data products - semantic layers, query/inference engines, BI and analytics platforms, or large-scale data pipelines - and you want to define the architecture of a category-defining product, this is for you.

What You'll Do

• Own the end-to-end architecture of WALT's analytical and data products - the context layer (ReasonBase™), the deterministic inference engine, and the data platform the agents operate on.
• Design, implement and evaluate systems that produce correct, reproducible, and auditable analytical results across large, heterogeneous enterprise datasets.
• Make the foundational technical decisions: data modeling and the semantic/knowledge graph, query and inference execution, storage and compute, orchestration across the agent framework, and the boundaries between neural and symbolic components.
• Partner with FDEs and founding engineers to translate ambiguous customer problems in various industries into robust, scalable technical designs.
• Set and raise engineering standards for performance, reliability, security, and compliance (auditability, lineage, governance) across the platform.
• Implement, and evaluate core components yourself; write reference implementations and review critical code.
• Mentor engineers, lead architectural reviews, and grow the technical maturity of the team.

What We're Looking For

Core experience

• 10+ years building software, with significant time spent architecting data-intensive and analytical products (analytics/BI platforms, semantic layers, query or inference engines, data warehouses/lakes, or large-scale data pipelines).
• Proven track record as an architect or principal/staff-level engineer who has owned the design of a complex product or platform end to end, shipped it externally, and seen it run in production at large enterprises - ideally built at a data or product company (e.g., Fivetran, dbt, Snowflake).
• Deep expertise in distributed systems and data engineering: data modeling, query processing, storage/compute trade-offs, and building for correctness and scale.
• Strong grasp of the analytics stack - SQL and modern data warehouses (e.g., Snowflake, BigQuery, Databricks), transformation frameworks (e.g., dbt), and orchestration.
• Fluency in one or more backend languages used for data systems (e.g., Python, Java/Scala, Go, or Rust).
• Ability to reason rigorously about data correctness, reproducibility, and auditability - you care that the answer is right and explainable, not just fast.
• Hands-on experience building with agentic AI - designing, orchestrating, and shipping production systems built on LLM/agent frameworks and multiple collaborating agents.

How you work

• Hands-on: you still design by building, and you earn technical trust through code and prototypes.
• Comfortable with ambiguity and an early-stage pace; you empathize with customers and go deep to solve their real problems, turning a fuzzy need into shipped software rather than a slide.
• A clear communicator and mentor who elevates the engineers around you.

Nice to have

• Experience with semantic layers, knowledge graphs, ontologies, or symbolic/rule-based reasoning
• Experience with ML/LLM systems (neuro-symbolic approaches). Being able to train or fine-tune models.
• Background delivering software into regulated or data-sensitive industries (financial services, retail/adtech, healthcare) with real compliance, governance, and security requirements.
• Experience as a technical leader or principal architect who has built data products from the ground up and scaled them to serve large enterprise customers.

Why WALT

• Define the architecture of a genuinely new category - the autonomous data engineer - from an early and influential seat.
• Work on hard, meaningful problems where correctness and trust are the product, not an afterthought.
• Direct impact with enterprise customers in high-stakes, regulated domains.
• A team that values reliability, security, and doing the hard engineering right.

On-site in Santa Clara, CA - five days a week in the office, full-time.

Authorized to work in the US without sponsorship - we are unable to sponsor employment visas for this role, now or in the future.