Data Engineer

Dashlabs.ai

2d•Remote

About The Position

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. 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.

Requirements

JavaScript, TypeScript, and Advanced SQL (Required).
Node.js, Express.js (MERN stack paradigm).
MongoDB (NoSQL) and Google BigQuery.
Looker Studio.
Proven experience building production-grade ETL/ELT pipelines converting nested NoSQL data structures (JSON/BSON) into optimized BigQuery schemas.
Deep understanding of database indexing, BigQuery billing/slot optimization, and architectural patterns used to separate transactional (OLTP) and analytical (OLAP) workloads.
Experience managing user roles, credentials, and data source permissions within cloud-based BI and Google Cloud environments (IAM).
Strong commitment to data privacy, security best practices, and handling sensitive healthcare data especially the Data Privacy Act of 2012.
Ability to write self-documenting code, design clean APIs, and work effectively in a fast-paced, high-growth startup environment.
Excellent communication skills.

Responsibilities

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.
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, optimized BigQuery views, and data sources that empower internal teams and external clients to build self-service reports.
Implement architectural safeguards (e.g., read replicas, change data capture, data lakes) to isolate analytics workloads and protect the primary production MongoDB database from resource-intensive data extraction queries.
Collaborate with the security and infrastructure teams to enforce strict data governance, access controls, and masking protocols for sensitive healthcare data before it reaches the data warehouse.
Write clean, maintainable, and typed code within our existing software ecosystem.
Develop backend services, scripts, and internal tools leveraging TypeScript and the MERN stack to facilitate automated data workflows, API integrations, and ETL orchestration.