Senior Data Engineer, LATAM
New
C
CleraData infrastructure
Fully remote for candidates based in Latin America.Full-TimeSenior
Salary80,000 - 120,000 USD per year
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Job Details
- Experience
- At least 5 years building and operating production data platforms across multiple sources
- Required Skills
- PostgreSQLPythonSQLGCPAirflowdbt
Requirements
- At least 5 years building and operating production data platforms across multiple sources.
- Own platform work from architecture through deployment and ongoing operations.
- Have hands-on production experience with dbt and Google Cloud Platform.
- Have workflow orchestration experience with Airflow, Dagster, Prefect, Temporal, or a comparable tool; Airflow is preferred.
- Have strong Python skills, including asynchronous HTTP and concurrent processing with asyncio, aiohttp, or HTTPX.
- Have strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading.
- Have experience building event-driven systems with messaging or task queues, asynchronous processing, and failure recovery.
- Have experience with data contracts, raw and curated storage, retention policies, automated testing, and production monitoring.
- Helpful experience includes web scraping, entity resolution, company and people data enrichment, and LLM-based extraction.
- Helpful experience includes building platforms from scratch in small teams.
Responsibilities
- Design a multi-source data platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows.
- Build intake pipelines for APIs, webhooks, files, and web scraping, with rate limits and failure handling.
- Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect.
- Develop messaging and task queue systems with retries, dead-letter queues, and controlled replay.
- Preserve source payloads in Google Cloud Storage and transform data with dbt into raw, standardized, and curated layers.
- Implement idempotent upserts, data contracts, automated validation, pipeline tests, and monitoring for freshness and failures.
- Design and optimize PostgreSQL schemas and loading patterns.
- Integrate enrichment and LLM-based structured extraction.
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