Engineering Team Lead (SDE 3)

We build systems that take messy, high-volume enterprise data and turn it into something correct and machine-readable at the other end. The hard parts are durability processes that run for days, survive anything and scale, with individual datasets running into the millions of rows. Language models sit inside these systems as a reasoning step, never as the thing doing the heavy lifting.

We are looking for the person who will own how this gets built. You would lead a small engineering team, work directly with the founder on architecture, and be accountable for the system being right, not merely for it shipping.

Responsibilities:

• Architecture execution. The core positions are settled: models reason over summaries, samples and clustered exceptions and emit specifications; deterministic code executes those specifications over the full dataset. A durable workflow engine owns the long-lived process; agent orchestration stays inside a single step and holds no state that must survive a restart. You will hold those boundaries as the system grows, and argue with us where you think we are wrong.
• Polyglot workers. TypeScript workflow definitions alongside the NestJS service layer, Python data and agent activities on a separate task queue. Keeping workflow code deterministic and bulk data out of it is your line to defend.
• The data engine. Polars and DuckDB over Parquet, streaming. No Spark, no Databricks; this fits in one container and scales by queue depth and horizontal replicas. Making that hold at millions of rows is a real engineering problem.
• Evaluation. The system's output is not obviously wrong when it is wrong, and there is no human downstream to catch it. A regression harness over historical input/output pairs is what keeps us honest, and it is load-bearing infrastructure rather than a testing nicety. You will build it early.
• The team. Hiring alongside you, code review, setting the bar for what gets merged, and growing a junior engineer into someone senior.
• Production. Development in our own environment, production inside customer cloud tenants. Containerised, portable, and yours to keep running.

Requirements:

• 5-7 years building backend or data-intensive systems, with at least one system you took from design to production and then lived with afterwards.
• Deep TypeScript or deep Python, and real working competence in the other. This is a genuinely polyglot codebase.
• You have designed a distributed system with long-running state and understand why that is hard: idempotency, retries, exactly-once as a fiction, what happens when a process dies mid-flight on day four.
• Serious data engineering instincts. You have hit the wall where the obvious approach stops working at volume, and you know what you did next.
• Experience leading engineers: reviewing their work, unblocking them, and telling them when something is not good enough without making it personal.
• Judgment about correctness. You are the kind of engineer who asks "how would we know if this were silently wrong? " before shipping.
• Temporal, or any durable-execution or workflow-orchestration engine in production.
• LLM systems where accuracy was measurable and measured: retrieval, structured extraction, agentic pipelines with evaluation behind them.
• Columnar and out-of-core processing: Polars, DuckDB, Arrow, Parquet.
• Azure, or cloud work inside an enterprise tenant with real security and data-residency constraints.
• Tech stack: NestJS Temporal LangGraph Python Polars / DuckDB Parquet Pydantic v2 Postgres Redis LiteLLM React + Vite Azure Docker Terraform / Bicep Alembic, OpenTelemetry.