Senior Data Engineer
New
C
CleraData infrastructure
Remote, candidates must be 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
- Required Skills
- PostgreSQLPythonSQLGCPAirflowdbt
Requirements
- Have at least 5 years of experience building and operating production data platforms that integrate multiple sources.
- Have owned data platforms from architecture through deployment and ongoing operations.
- Have production experience with dbt and Google Cloud Platform.
- Have experience with workflow orchestration using Airflow, Dagster, Prefect, Temporal, or a comparable tool; Airflow is preferred.
- Bring strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
- Bring 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 raw and curated storage, retention policies, data validation, pipeline testing, and monitoring.
- Startup or small-team experience and broad ownership are valuable.
- Web scraping, entity resolution, data enrichment, LLM-based extraction, BigQuery, and Dataform experience are useful.
Responsibilities
- Design and build a multi-source data platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows.
- Ingest data from provider APIs, webhooks, files, and web scraping, with rate limiting and failure handling.
- Orchestrate scheduled workflows and historical backfills using workflow orchestration tools.
- Build event-driven systems with messaging and task queues, including retries, dead-letter queues, and controlled replay.
- Preserve original payloads in cloud storage and transform them 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 providers and LLM-based structured extraction.
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