Data engineer (databricks & aws)
remote (latin america / europe) | 9 am - 5 pm est | full-time
at cloudgeometry, we partner with industry leaders like aws, google, and databricks to deliver cutting-edge cloud-native solutions. We are looking for a senior data engineer to join our flagship project: a modern data platform for the life sciences industry, supporting global leaders like pfizer, moderna, and novartis in developing innovative rna-based solutions using cloud computing and advanced ai.
if you are an experienced data engineer who thrives in high-impact environments, zeroes out legacy systems, and wants to play a key technical leadership role in building scalable lakehouse architectures, let’s talk!
key responsibilities
* pipeline engineering: design, develop, and optimize high-performance etl pipelines within databricks to connect analytics-ready data back to operational services.
* architecture leadership: lead technical architecture discussions with engineering, product managers, and data scientists to implement advanced analytics.
* workflow optimization: build, fine-tune, and monitor databricks workflows to ensure system reliability, performance, and data integrity.
* data quality & security: collaborate with ml teams to ensure secure, rigorous, and accurate data ingestion across all processing stages.
* agile execution: actively participate in daily scrum ceremonies within a globally distributed engineering team.
️ technical requirements & stack
1. Core data engineering
* databricks ecosystem: 2+ years of hands-on experience (delta tables/iceberg, spark jobs, mlflow, unity catalog, model registry).
* architecture: expert-level understanding of modern lakehouse architectural design principles.
* languages: expert-level python (for data processing/etl) and typescript / node.js (for backend services using hapijs, zod, and jest).
2. Cloud infrastructure & devops (aws)
* compute & storage: ecs (fargate/ec2), lambda, s3, and athena.
* messaging & orchestration: sqs/sns and airflow.
* devops & ci/cd: github actions, codebuild, docker, and repository templates via cruft.
3. Data stores & mlops
* databases: postgresql (acid/migrations), dynamodb (high-scale key-value), and redis (caching/rate limiting).
* search: opensearch / elasticsearch for full-text search and aggregations.
* genai: practical knowledge of llms, agents, function calling, and rag architectures.
qualifications
* experience: 5+ years in software development with a strong focus on data engineering/analytics teams.
* senior autonomy: proven ability to challenge decisions, propose architectural improvements, and deliver complex features end-to-end.
* communication: exceptional english skills (written and spoken) to articulate complex data ideas to global stakeholders.
* availability: required online presence from 9 am to 5 pm est .
⭐ nice to have:
* professional databricks or aws certifications.
* experience building internal sdks or developer experience tooling.
* experience working directly alongside data scientists and ml developers.
what we offer (our commitment to you)
* comprehensive compensation and benefits package.
* ⚡ zero legacy systems – work exclusively with cutting-edge technologies.
* continuous learning: extensive training, certifications, hackathons, and udemy access.
* ️ premium tooling: developer pro access to claudecode, codex, and antigravity.
* top-tier culture: a collaborative, supportive environment with global experts.
ready to build the future of life sciences?
click apply or send us your resume. Let’s build something massive together!
#dataengineering #databricks #aws #python #typescript #remotejobs #lakehouse