Dashlabs. ai
Healthcare
DataEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Data Engineer at Dashlabs. ai. Skills: Data Pipeline Engineering, Data Warehousing, Business Intelligence, Database Administration. Design data pipelines. Implement data pipelines”
Industry & Context.
What They're Looking For.
Must Have
Advanced SQL required, JavaScript required, TypeScript required, Node.js required, Express.js required, MongoDB required, Google BigQuery required, Looker Studio required, ETL/ELT pipelines experience, NoSQL to BigQuery schema conversion experience, Database indexing understanding, BigQuery billing optimization understanding, Transactional and analytical workload separation understanding, Cloud-based BI user role management experience, Google Cloud IAM experience, Data privacy commitment, Security best practices commitment, Sensitive healthcare data handling experience, Data Privacy Act of 2012 knowledge, Self-documenting code ability, Clean API design ability, Fast-paced startup environment work ability, Excellent communication skills
Nice to Have
MERN stack expertise, Data warehousing expertise, ETL/ELT pipeline expertise, Database administration expertise, PhD preferred, Specific ML framework experience, Cloud platform certs
What You'll Do.
Design data pipelines
Implement data pipelines
Maintain data pipelines
Replicate operational data
Transform operational data
Ensure data consistency
Ensure data integrity
Ensure low-latency synchronization
Optimize BigQuery storage
Optimize BigQuery partitioning
Optimize BigQuery clustering
Optimize BigQuery query performance
Manage BI data enablement pipeline
Construct semantic layers
Maintain semantic layers
Construct BigQuery views
Maintain BigQuery views
Construct data sources
Maintain data sources
Implement architectural safeguards
Isolate analytics workloads
Protect production MongoDB database
Collaborate on data governance
Enforce access controls
Enforce data masking protocols
Develop backend services
Develop internal tools
Facilitate automated data workflows
Facilitate API integrations
Facilitate ETL orchestration
How You'll Work.
Team & Collaboration
Security teams; Infrastructure teams
Communication Scope
Communication skills
Full Job Description
Dashlabs.ai (YC W21) is a healthcare operations platform that integrates Electronic Medical Records (EMR), Laboratory Information Systems (LIS), and Radiology Information Systems (RIS) into a unified ecosystem. Built for diagnostic laboratories, imaging centers, clinics, and hospitals, Dashlabs.ai serves as the operational backbone for diagnostic providers across the Philippines and is expanding its footprint into Indonesia and Malaysia. We build robust, scalable infrastructure to streamline healthcare workflows, optimize diagnostics, and improve patient outcomes. Role Overview As a Data Engineer, you will design, build, and optimize the data infrastructure that powers our reporting, analytics, and business intelligence capabilities. You will manage data synchronization across disparate database paradigms, enable stakeholders through modern BI tools, and play a critical role in safeguarding our core production databases from intensive analytical workloads and unauthorized data extraction. This role requires a strong engineering background with a deep understanding of the MERN stack, particularly TypeScript, alongside robust database administration, data warehousing, and ETL/ELT pipeline expertise. Key Responsibilities 1. Data Pipeline Engineering & ELT * Design, implement, and maintain scalable data pipelines to replicate and transform operational data from a production MongoDB environment into Google BigQuery for analytics and data warehousing. * Ensure data consistency, integrity, and low-latency synchronization between NoSQL documents and BigQuery's columnar storage structures. * Optimize BigQuery storage, partitioning, clustering, and query performance to minimize operational costs and speed up retrieval times. 2. Analytics & Business Intelligence Infrastructure * Own the onboarding, access management, and data enablement pipeline for users on Looker Studio , ensuring seamless integration with BigQuery datasets. * Construct and maintain semantic layers, optimize
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