Percepta
Who we are
Percepta’s mission is to
transform critical institutions with applied AI.
We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.
To make that happen, we embed with industry‑leading customers to drive AI transformation. We bring together:
Forward‑deployed expertise in engineering, product, and research
Mosaic, our in‑house toolkit for rapidly deploying agentic workflows
Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more
Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers, and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day‑to‑day lives.
Percepta is a direct partnership with General Catalyst, a global transformation and investment company.
About the role We’re hiring a
Data Platform Lead / Data Architect
to design, build, and operationalize the data platforms that power AI transformation across large, complex enterprises. You will lead decisions around data models, infrastructure, orchestration, quality, and governance: enabling Applied AI Engineers to ship high‑impact AI workflows quickly and safely.
You’ll work hands‑on inside messy, heterogeneous enterprise environments, unifying systems across cloud platforms, operational databases, legacy applications (ERPs, EHRs, and more). Your work will help create the core data and intelligence technologies that Percepta deploys across many customer environments.
If you enjoy building in ambiguity, forming strong technical opinions, owning architecture end‑to‑end, and enabling AI at enterprise scale, this role is for you.
In this role, you will:
Own the architecture
of foundational data platforms that support dozens of AI workflows.
Shape Percepta’s data strategy
across multiple customer environments, cloud providers, and system landscapes.
Lead cloud migration patterns , helping customers modernize their data stack while ensuring operational reliability.
Build reusable platform components
that become playbooks for future enterprise deployments.
Partner directly with product teams
to translate high‑value use cases into crisp data requirements, schemas, and pipeline needs.
Work with operators, engineering teams, and leadership
to turn high‑value use cases into production‑ready data workflows.
What you’ll do
Build the intelligence layer that unlocks entirely new agentic AI workflows across healthcare and other critical industries.
Architect and run end‑to‑end data platforms — from schema design to pipelines to the storage and retrieval patterns that power real‑time AI.
Bring order to chaos by translating fragmented enterprise systems into clean, usable, high‑leverage data assets.
Work side‑by‑side with AI engineers, operators, and delivery partners to ship production systems that create measurable business impact.
What we’re looking for Strong technical foundations
Deep experience building pipelines on Databricks, Snowflake, or similar cloud platforms
SQL and Python proficiency
Familiarity with streaming tools (Kafka, Kinesis, etc.)
Strong understanding of ETL/ELT, orchestration, CI/CD for data, and schema design
Experience working across hybrid cloud or modernization environments
Experience building or owning enterprise data systems
Designed platform‑level data architectures
Built foundational data models and semantic/intelligence layers
Mentored or led data engineers (formal or informal leadership)
Worked across distributed systems, operational DBs, and analytics warehouses
Led build / buy / partner processes and vendor evaluation to make strategic decisions regarding data roadmap
AI and ML intuition
Understanding of what ML and LLM systems need (features, context windows, retrieval patterns, embeddings)
Experience supporting ML pipelines or AI‑adjacent data flows
Thrives in ambiguity & forward‑deployed environments
Comfortable building in messy, fragmented enterprise systems
Capable of unblocking yourself with high ownership
Excellent communication with comfort talking directly to customer teams
Bias toward action while also thinking long‑term
Bonus if you have
Prior startup or forward‑deployed engineering experience
Experience with AWS or Azure
Experience leading data modernization or cloud migration initiatives
Background in building data platforms across multiple business units or customer environments
Experience working with health systems (or similar legacy enterprises) - Experience integrating ERP, EHR, CAPS, Claims, and ancillary sources into a unified data systems
#J-18808-Ljbffr
transform critical institutions with applied AI.
We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.
To make that happen, we embed with industry‑leading customers to drive AI transformation. We bring together:
Forward‑deployed expertise in engineering, product, and research
Mosaic, our in‑house toolkit for rapidly deploying agentic workflows
Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more
Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers, and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day‑to‑day lives.
Percepta is a direct partnership with General Catalyst, a global transformation and investment company.
About the role We’re hiring a
Data Platform Lead / Data Architect
to design, build, and operationalize the data platforms that power AI transformation across large, complex enterprises. You will lead decisions around data models, infrastructure, orchestration, quality, and governance: enabling Applied AI Engineers to ship high‑impact AI workflows quickly and safely.
You’ll work hands‑on inside messy, heterogeneous enterprise environments, unifying systems across cloud platforms, operational databases, legacy applications (ERPs, EHRs, and more). Your work will help create the core data and intelligence technologies that Percepta deploys across many customer environments.
If you enjoy building in ambiguity, forming strong technical opinions, owning architecture end‑to‑end, and enabling AI at enterprise scale, this role is for you.
In this role, you will:
Own the architecture
of foundational data platforms that support dozens of AI workflows.
Shape Percepta’s data strategy
across multiple customer environments, cloud providers, and system landscapes.
Lead cloud migration patterns , helping customers modernize their data stack while ensuring operational reliability.
Build reusable platform components
that become playbooks for future enterprise deployments.
Partner directly with product teams
to translate high‑value use cases into crisp data requirements, schemas, and pipeline needs.
Work with operators, engineering teams, and leadership
to turn high‑value use cases into production‑ready data workflows.
What you’ll do
Build the intelligence layer that unlocks entirely new agentic AI workflows across healthcare and other critical industries.
Architect and run end‑to‑end data platforms — from schema design to pipelines to the storage and retrieval patterns that power real‑time AI.
Bring order to chaos by translating fragmented enterprise systems into clean, usable, high‑leverage data assets.
Work side‑by‑side with AI engineers, operators, and delivery partners to ship production systems that create measurable business impact.
What we’re looking for Strong technical foundations
Deep experience building pipelines on Databricks, Snowflake, or similar cloud platforms
SQL and Python proficiency
Familiarity with streaming tools (Kafka, Kinesis, etc.)
Strong understanding of ETL/ELT, orchestration, CI/CD for data, and schema design
Experience working across hybrid cloud or modernization environments
Experience building or owning enterprise data systems
Designed platform‑level data architectures
Built foundational data models and semantic/intelligence layers
Mentored or led data engineers (formal or informal leadership)
Worked across distributed systems, operational DBs, and analytics warehouses
Led build / buy / partner processes and vendor evaluation to make strategic decisions regarding data roadmap
AI and ML intuition
Understanding of what ML and LLM systems need (features, context windows, retrieval patterns, embeddings)
Experience supporting ML pipelines or AI‑adjacent data flows
Thrives in ambiguity & forward‑deployed environments
Comfortable building in messy, fragmented enterprise systems
Capable of unblocking yourself with high ownership
Excellent communication with comfort talking directly to customer teams
Bias toward action while also thinking long‑term
Bonus if you have
Prior startup or forward‑deployed engineering experience
Experience with AWS or Azure
Experience leading data modernization or cloud migration initiatives
Background in building data platforms across multiple business units or customer environments
Experience working with health systems (or similar legacy enterprises) - Experience integrating ERP, EHR, CAPS, Claims, and ancillary sources into a unified data systems
#J-18808-Ljbffr