Senior / Lead Platform Forward Deployed Engineer - Cogentiq I2C

The platform has crossed from being built to being deployed. Collections is entering pilot with enterprise clients, and the deployment machinery is real: Databricks Asset Bundles with one-click, script-based deployment; auto-loader ingestion running historical and incremental loads into a common data model; versioned bundles and wheel-file packages delivered through Git and Azure Artifactory; and a quick-start standard under which a newcomer must be able to stand up the full data platform by running one setup script. This role carries that machinery into client environments and makes it hold under client governance, client data, and client timelines. It owns everything between our release artefacts and a running, observable, correctly governed installation; candidates who want to work on agent behaviour and LLM integration should apply to the AI FDE track instead.

What you'll do:

• Own the deployment architecture of client installations from environment readiness through hypercare: bundle strategy, environment topology (Dev, QA, production; per-client catalogs), release sequencing, and sign-off that the installation meets the validation standard.
• Navigate Unity Catalog and platform governance Client and internal IT control catalog and metastore provisioning, storage decisions, and access models. You will assess what their governance model means for our setup scripts, negotiate provisioning paths, and know when a shared-infrastructure conveni
• Set and enforce the platform engineering standards: idempotent, rerunnable deployments; fail-fast error handling with no silent except-and-continue paths; environment-variable-driven parameterisation with a single point of change per client value; and configuration conventions that hold across Datab
• Own the shared utilities architecture: interface-function contracts and factory-pattern abstractions across Databricks, Fabric, PostgreSQL, SQL Server, and storage back ends, packaged as versioned wheel files through Azure Artifactory, so swapping a client's platform is a configuration change, not a
• Make the hard ingestion calls: auto-loader versus custom conflict-driven ingestion when the client will not provision Databricks, checkpoint and reprocessing strategy, retry and alerting design per pipeline, and writeback reliability to the application database.
• Run incident command during deployment and hypercare. When a deployment breaks in a client environment, you run the incident: triage across the data estate, the application database, and upstream ERP feeds; communicate honestly with the client; and land the fix and the post-incident correction to th

Full details and application on the official Fractal careers page.