Senior Software Engineer (Backend, Data and AI)

WALT (thewalt.ai) is building the world’s first Autonomous Data Engineer: a platform of five specialised, collaborating AI agents (Ingestor, Transformer, Reasoner, Operator, and Governor) that manage an entire data platform end-to-end, covering 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 Senior Software Engineer to help build the data and analytical products at the core of WALT, 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’ll design, implement, and evaluate core components of how our agents reason over data, build the platform that makes that reasoning fast, correct, and governable, and go deep to prove out the hard parts in code. You’ll be a strong individual contributor and a key technical voice as WALT scales, a force multiplier for the engineering team.

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 build a category-defining product hands-on, this is for you.

What You’ll Do

• Design, implement, and evaluate core components of WALT’s analytical and data products: the context layer (ReasonBase™), the deterministic inference engine, and the data platform the agents operate on.
• Build systems that produce correct, reproducible, and auditable analytical results across large, heterogeneous enterprise datasets.
• Contribute to the foundational technical decisions: data modelling 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 across industries into robust, scalable implementations.
• Uphold and raise engineering standards for performance, reliability, security, and compliance (auditability, lineage, governance).
• Own critical services end-to-end, writing reference implementations and reviewing important code.
• Mentor engineers and contribute to design and code reviews as the team grows.

Core experience

• 5+ years building production software, with meaningful time spent on data-intensive and analytical products (analytics/BI platforms, semantic layers, query or inference engines, data warehouses/lakes, or large-scale data pipelines).
• A track record of owning and shipping complex components or services end-to-end, seen running in production, ideally at a data or product company (e.g., Fivetran, dbt, Snowflake).
• Solid foundation in distributed systems and data engineering: data modelling, 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 (or strong, demonstrated interest) building with agentic AI: designing, orchestrating, and shipping systems built on LLM/agent frameworks and multiple collaborating agents.

How you work

• Hands-on: you design by building and earn technical trust through code and prototypes.
• Comfortable with ambiguity and an early-stage pace. You empathise with customers and go deep to solve their real problems, turning a fuzzy need into shipped software rathe…