About the role
Who we are Sidian is disrupting architecture, engineering and construction (AEC). We began September 2025 with mid-sized engineering firms, raised a US$1M pre-seed round at the end of May and are now moving into enterprise, with 3 of the top 30 global firms entering the platform in 3 months. Our mission is to lead AEC's technological revolution, bringing the same step-change already reshaping medicine, finance and law. Our purpose is simple: to make humanity's basic needs affordable and sustainable, starting with infrastructure.
The role Engineers today work with data trapped in silos 3D models, BIM files, PDF drawings, spreadsheets, Word documents, emails — that not even other engineers, let alone AI, can easily read across. At Sidian, we're building the company's brain that brings all the engineering firm's knowledge to their fingertips.
As our Lead Data Engineer, you'll design and build the pipelines that ingest these diverse, messy engineering data types and unify them into a structured, queryable knowledge graph. You'll ensure that every engineering decision is captured, connected, and retrievable — not just going forward, but reconstructed from past projects as well. The goal: make hard-won engineering knowledge a durable, leverageable asset rather than something locked in files no one can find.
You'll work directly with the founders, own core architecture, ship product, learn from customers, and shape the engineering culture. You'll turn fast-moving AI capabilities into dependable systems. As a critical early team member, you'll experience rapid growth with evolving opportunity as the company scales.
What you'll own Build and improve the multimodal ingestion pipelines that transform drawings, BIM, PDFs, spreadsheets, documents and project history into reliable, structured data. Architect and advance the company knowledge graph so engineering decisions across current and past projects are tracked, connected, and retrievable. Develop the retrieval and reasoning systems on top of the graph that make this knowledge usable for search, recommendations, and automation. Design the data models and polyglot storage that hold heterogeneous engineering data together at scale. Work with customers to turn ambiguous engineering workflows into simple, dependable products.
What you bring Strong data engineering skills in Python, with sound judgment across backend and data systems. Hands-on experience with knowledge graphs and graph databases (Neo4j, Cypher), including modeling complex relationships. Experience building production data pipelines over modern infrastructure: Apache Spark, Databricks, Parquet, and polyglot / multi-store architectures. Comfort ingesting and parsing messy, heterogeneous formats (3D/BIM, PDF drawings, Excel, Word, email) into structured data. Evidence of initiative — an independent project, open-source contribution, research build, or startup you drove from idea to working system. High agency, product judgment, and clear communication. AEC experience is not required.
About Sidian
Building Sid, the autonomous engineer.
30% of every engineer's day disappears into redoing work and searching for information that already exists somewhere inside the firm, buried across PDFs, drawings, specs, and disconnected file systems. Worse, the same calculations get run, the same details get drafted, and the same mistakes get made, project after project, because there is no system connecting past work to present decisions.
Sid changes that at the infrastructure level. A custom data processing pipeline autonomously indexes your entire project archive, structuring decades of work into a queryable intelligence layer built on fine-tuned models trained specifically on engineering data. On top of that sits a desktop agent that operates directly inside your existing tools, running calculations, generating documentation, pulling code references, and executing the repetitive technical work that consumes an engineer's day. Not a chatbot you ask questions to. A system that acts, inside the tools you already use, on your behalf.
Every project your firm has ever completed becomes active leverage for every project it takes on next.
Engineer the extravagant. Automate the algorithmic.
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About the role
Who we are Sidian is disrupting architecture, engineering and construction (AEC). We began September 2025 with mid-sized engineering firms, raised a US$1M pre-seed round at the end of May and are now moving into enterprise, with 3 of the top 30 global firms entering the platform in 3 months. Our mission is to lead AEC's technological revolution, bringing the same step-change already reshaping medicine, finance and law. Our purpose is simple: to make humanity's basic needs affordable and sustainable, starting with infrastructure.
The role Engineers today work with data trapped in silos 3D models, BIM files, PDF drawings, spreadsheets, Word documents, emails — that not even other engineers, let alone AI, can easily read across. At Sidian, we're building the company's brain that brings all the engineering firm's knowledge to their fingertips.
As our Lead Data Engineer, you'll design and build the pipelines that ingest these diverse, messy engineering data types and unify them into a structured, queryable knowledge graph. You'll ensure that every engineering decision is captured, connected, and retrievable — not just going forward, but reconstructed from past projects as well. The goal: make hard-won engineering knowledge a durable, leverageable asset rather than something locked in files no one can find.
You'll work directly with the founders, own core architecture, ship product, learn from customers, and shape the engineering culture. You'll turn fast-moving AI capabilities into dependable systems. As a critical early team member, you'll experience rapid growth with evolving opportunity as the company scales.
What you'll own Build and improve the multimodal ingestion pipelines that transform drawings, BIM, PDFs, spreadsheets, documents and project history into reliable, structured data. Architect and advance the company knowledge graph so engineering decisions across current and past projects are tracked, connected, and retrievable. Develop the retrieval and reasoning systems on top of the graph that make this knowledge usable for search, recommendations, and automation. Design the data models and polyglot storage that hold heterogeneous engineering data together at scale. Work with customers to turn ambiguous engineering workflows into simple, dependable products.
What you bring Strong data engineering skills in Python, with sound judgment across backend and data systems. Hands-on experience with knowledge graphs and graph databases (Neo4j, Cypher), including modeling complex relationships. Experience building production data pipelines over modern infrastructure: Apache Spark, Databricks, Parquet, and polyglot / multi-store architectures. Comfort ingesting and parsing messy, heterogeneous formats (3D/BIM, PDF drawings, Excel, Word, email) into structured data. Evidence of initiative — an independent project, open-source contribution, research build, or startup you drove from idea to working system. High agency, product judgment, and clear communication. AEC experience is not required.
About Sidian
Building Sid, the autonomous engineer.
30% of every engineer's day disappears into redoing work and searching for information that already exists somewhere inside the firm, buried across PDFs, drawings, specs, and disconnected file systems. Worse, the same calculations get run, the same details get drafted, and the same mistakes get made, project after project, because there is no system connecting past work to present decisions.
Sid changes that at the infrastructure level. A custom data processing pipeline autonomously indexes your entire project archive, structuring decades of work into a queryable intelligence layer built on fine-tuned models trained specifically on engineering data. On top of that sits a desktop agent that operates directly inside your existing tools, running calculations, generating documentation, pulling code references, and executing the repetitive technical work that consumes an engineer's day. Not a chatbot you ask questions to. A system that acts, inside the tools you already use, on your behalf.
Every project your firm has ever completed becomes active leverage for every project it takes on next.
Engineer the extravagant. Automate the algorithmic.