Data Platform and Infrastructure Engineer

Cognito Therapeutics is hiring a Data Platform and Infrastructure Engineer to build and operate a trusted cloud data platform bringing together device, clinical, research, and business data. The role covers data ingestion and lakehouse architecture, analytics and BI enablement, chatbot integration, and security, privacy, and governance.

Responsibilities:

• Design and build the cloud infrastructure — compute, storage, and networking — underpinning Cognito's data platform, partnering with the IT Director on architecture and technology choices
• Build ingest points and pipelines that reliably bring data in from multiple sources — device and telemetry data, clinical and research data, and core business systems — into the central platform at scale
• Help solve the technical challenges of getting disparate datasets to “speak the same language” — designing common data models, schemas, and data contracts so device, clinical, and business data can be joined and reasoned about together
• Establish data quality and validation checks at ingestion so downstream users and systems can trust the data
• Own the technical setup of the data lakehouse — storage layout, table formats, partitioning, and compute engine configuration — balancing cost, performance, and ease of access
• Build pipeline reliability and observability practices — monitoring, alerting, and data quality checks — into the platform from the start
• Establish de-identification of data, ensuring proper handling of all Personally Identifiable and Protected Health Information
• Stand up the analytics layer on top of the lakehouse — a semantic/metrics layer, curated data marts, and the infrastructure needed for self-serve analytics
• Partner with business, clinical, and data science stakeholders to understand their analytics needs and translate them into platform capabilities
• Set up and support a traditional BI interface (dashboards and reporting) so business stakeholders can access insights without needing to write queries themselves
• Build, or lead the technical integration of, an internal chatbot
• Ensure access controls, encryption, and data governance are built into the platform from the start, not bolted on later
• Document platform architecture and operational runbooks so the platform can scale as the team grows

Requirements:

• Bachelors degree in Computer Science, Software Engineering, or Information Systems with 5+ years building data platforms or data pipelines, including hands-on cloud infrastructure experience (e.g., AWS, Azure, or GCP) provisioning ingestion, storage, and compute for data workloads
• Experience designing and building data ingestion pipelines from multiple heterogeneous sources into a central platform
• Experience with data modeling or schema standardization work that makes disparate datasets interoperable (e.g., common data models, data contracts, or master data management)
• Hands-on experience building a data lakehouse architecture (e.g., Delta Lake, Apache Iceberg, or a similar table format), including storage layout, partitioning, and query performance
• Experience standing up or supporting a BI/analytics layer (e.g., dashboards, a semantic layer, or self-serve analytics tooling)
• Strong SQL and data engineering fundamentals — ETL/ELT, orchestration, and data quality practices
• Experience building or integrating an LLM-powered chatbot or natural-language interface over structured, unstructured, or analytical data
• All applicants must be legally authorized to work in the United States
• Experience with healthcare, clinical trial, or biosignal/medical device data specifically
• Experience building data platforms in a regulated environment (HIPAA, and eventually SOC 2/HITRUST)
• Familiarity with data governance or data cataloging tools and data quality frameworks
• Experience partnering with data science or ML teams on productionizing analytics or AI features