Resumo da vaga

Data Engineer

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

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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