Senior Data Engineer
At a glance
Senior Data Engineering role at Clera. Remote · full-time · $80,000–$120,000 base.
dataroles summary, based on the employer's posting.
What you'll do
- Build a platform that ingests data from multiple sources, handles events and runs scheduled batch workflows
- Create intake pipelines for APIs, webhooks, files and scraping, with rate limits and recovery from failures
- Set up scheduled runs and historical backfills with Airflow, Dagster, Prefect or another listed orchestrator
- Keep original data in Google Cloud Storage, then use dbt to shape it into raw, standardized and curated layers
- Make pipelines dependable with idempotent updates, data contracts, automated checks, tests and freshness monitoring
What you bring
- At least five years building and running production data platforms, from design through deployment and ongoing operations
- Hands-on production work with GCP and dbt, plus workflow orchestration using Airflow, Dagster or another listed tool
- Strong Python for asynchronous HTTP and concurrent processing, and strong SQL and PostgreSQL for modeling and incremental loads
- Experience with event queues, data contracts, storage and retention, automated testing and pipeline monitoring
Who this fits
This role suits a senior data engineer who can take responsibility for a platform across design, implementation and operations. You must be based in Latin America and able to work fully remotely. Visa sponsorship is unavailable.
From the employer
About the Role
Join a data infrastructure team building a platform that turns information from multiple sources into reliable, up-to-date company intelligence. You will own the platform end-to-end, from architecture and ingestion through production operations, helping power downstream products with trusted data.
What You'll Do
Design and build a multi-source data platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows.
Develop intake pipelines for provider APIs, webhooks, files, and web scraping, with rate limiting and robust failure handling.
Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect.
Build event-driven systems with messaging or task queues, including retries, dead-letter queues, and controlled replay.
Preserve original 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, and integrate enrichment providers and LLM-based structured extraction.
What We're Looking For
At least 5 years building and operating production data platforms, with ownership spanning architecture, implementation, deployment, and ongoing operations.
Production experience with GCP and dbt, plus workflow orchestration such as Airflow, Dagster, Prefect, or Temporal, including dependency management, retries, and recovery.
Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
Strong SQL and PostgreSQL skills in data modeling, query optimization, and reliable incremental loading.
Experience with event-driven systems, queues, data contracts, raw and curated storage, retention policies, automated testing, and pipeline monitoring.
Experience in a startup or small engineering team is useful. Web scraping, entity resolution, company or people data enrichment, and LLM-based extraction are also relevant.
Compensation & Benefits
Annual base salary: $80,000 to $120,000 USD. Visa sponsorship is not available.
Location
Fully remote, candidates must be based in Latin America.
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