Dashlabs.ai
Data Engineer
Remote Data Engineering role with clear candidate location fit.
PostedJul 5, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Philippines
Role overview
Data Engineer
Requirements and responsibilities
Readable role content extracted into sections for faster review.
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.
1. Data Pipeline Engineering & ELT
- 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.
3. Production Database Optimization & Security
- 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.
4. Full-Stack Data Integration (MERN Stack)
- 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.
Technical Stack Proficiencies:
- Languages: JavaScript, TypeScript, and Advanced SQL (Required).
- Frameworks: Node.js, Express.js (MERN stack paradigm).
- Databases & Warehousing: MongoDB (NoSQL) and Google BigQuery.
- BI Tools: Looker Studio.
Experience & Competencies:
- 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.
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