AI Engineer - Data

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

Job summary

WALT AI is seeking an AI-native software engineer who has built data products and will own the technical direction of WALT’s data and analytical offerings. You will ingested, transformed, and reason over enterprise data with five collaborating agents, ensuring correctness, security, and scalability.

The role emphasizes hands-on design and implementation, mentoring, and leading architectural reviews to scale the platform for external customers.

Qualifications

• 10+ years building software, with significant time spent creating data-intensive and analytical products.
• Proven track record as a principal/staff-level engineer or architect who shipped to external customers and supported in production.
• Deep expertise in distributed systems, data systems internals, and data modeling.
• Strong fundamentals in API design, testing, CI, and release engineering.
• Fluency in backend languages such as Python, Java/Scala, Go, Rust, or C++.
• Hands-on experience building with agentic AI and multi-agent frameworks.

Responsibilities

• Own the end-to-end architecture of WALT’s data and analytical products across all five agents.
• Design, implement and evaluate systems that produce correct, reproducible, auditable results.
• Build the ingestion layer with connectors, CDC, schema drift detection, and exactly-once delivery.
• Build the transformation layer, including data contracts, incremental processing, and lineage.
• Design interfaces for customers’ data engineers to connect sources and review pipelines.
• Set engineering standards for API design, testing, release, performance, and security.

Skills

Data-intensive engineering

Distributed systems

Backend development

Agentic AI experience

Mentoring/leadership

Fluency in Python/Java/Scala

Hands-on architecture

Tools

Python

Java/Scala

Go

Rust

C++

Job description

Company: WALT AI Team: Engineering Reports to: CTO Location: Santa Clara, CA (on-site, 5 days a week) Type: Full-time

Company: WALT AI Team: Engineering Reports to: CTO Location: Santa Clara, CA (on-site, 5 days a week) Type: Full-time

About WALT

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 trust at every step. The Ingestor brings in data from any source on any schedule, and the Transformer turns it into clean, tested, versioned models. The Reasoner then answers questions with deterministic inference over a Data Context Graph (ReasonBase) that maps business vocabulary to that data, not an LLM writing SQL on the fly, so the answer is the same every time. That is why regulated customers in financial services, retail, and advertising trust us with their analytics: 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 who has built data products : software that data engineers and analysts at other companies install, query, and depend on. You’ll own the technical direction of WALT’s data and analytical products, the systems that ingest raw, messy enterprise data from any source, transform it into clean, governed models, and turn it into trusted, deterministic answers at scale.

Our users are data engineers, BI engineers, and analysts. You won’t be building the pipelines they would otherwise build by hand. You’ll be building the product that builds and runs those pipelines for them and answers their business questions the same way every time. That means caring about API design, versioning, latency, testing, and failure modes as much as about data modeling.

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 ingest, transform, and reason over data, design the platform that makes each of those steps 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 data products that other engineers use, such as ingestion and connector platforms, transformation frameworks, orchestration and scheduling systems, query or inference engines, semantic layers, or analytics platforms, and you want to define the architecture of a category-defining product, this is for you. If most of your experience is building and running pipelines for your own company’s analytics, this role is probably not the right fit.

What You’ll Do

• Own the end-to-end architecture of WALT’s data and analytical products across all five agents: ingestion, transformation, the context layer (ReasonBase), the deterministic inference engine, and the data engineering components the agents run on, including lineage, job scheduling, and the CI/CD builder for customer pipelines, plus the APIs customers build on.
• Design, implement and evaluate systems that produce correct, reproducible, and auditable analytical results across large, heterogeneous enterprise datasets.
• Build the ingestion layer: connectors that pull from any source and format (databases, SaaS APIs, files, event streams) on any schedule, with change data capture, schema drift detection, backfills, and exactly-once delivery.
• Build the transformation layer: how the agents generate, test, and version the models that clean, normalize, and reshape raw data, with data contracts, incremental processing, and column-level lineage from source to final metric.
• Design the interfaces our customers' data engineers work with: how they connect sources, review and version the pipelines and models the agents generate, define metrics, and query results, and what happens when a source schema or a definition changes under a live pipeline, dashboard, or agent.
• Hold the product to interactive latency and reliability targets inside customer environments, including multi-tenancy, security, and safe upgrades.
• Make the foundational technical decisions: ingestion and change-data-capture design, transformation and data modeling, the semantic/knowledge graph, query and inference execution, storage and compute, orchestration across the agent framework, and the boundaries between neural and symbolic components.
• Work directly with customers to turn their ambiguous problems across industries into product capabilities that work for every customer, not one-off solutions.
• Set and raise engineering standards for API design, testing, release, performance, reliability, security, and compliance (auditability, lineage, governance) across the product.
• Build 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 creating data-intensive and analytical products : ingestion or connector platforms, transformation and orchestration frameworks, query or inference engines, semantic layers, analytics/BI platforms, or other data infrastructure and developer tools used by engineers at other companies.
• Proven track record as a principal/staff-level engineer or architect who has owned the design of a product end to end, shipped it to external customers , and supported it in production at large enterprises. Experience building at a data or developer-tools product company (e.g., Snowflake, Databricks, dbt Labs, Fivetran, Airbyte, DuckDB/MotherDuck, Cube) is a strong plus.
• Deep expertise in distributed systems and data systems internals : ingestion and change data capture, incremental and streaming processing, data modeling, query planning and execution, storage/compute trade-offs, concurrency, and performance, built for correctness and scale.
• Strong software engineering fundamentals : API and interface design, versioning and backwards compatibility, testing, CI, and release engineering. You have built interfaces other engineers depend on and changed them without breaking the people who use them.
• Fluency in one or more backend languages used to build data systems (e.g., Python, Java/Scala, Go, Rust, or C++).
• 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
• Working knowledge of the analytics stack your users run : SQL warehouses (e.g., Snowflake, BigQuery, Databricks), transformation frameworks (e.g., dbt), and orchestration.
• 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.