Senior Data Engineer
At a glance
Senior Data Engineering role at Clera. Remote · full-time · $80,000–$120,000 base.
remoteroles summary, based on the employer's posting.
What you'll do
- Build a platform that ingests data from several sources, handles event streams and runs coordinated batch jobs
- Create intake pipelines for APIs, webhooks, files and scraped pages, with rate controls and recovery from failures
- Set up scheduled processing and historical reloads, plus queued event handling with retries and controlled replay
- Store source payloads in Google Cloud Storage, then use dbt to produce raw, standardized and curated data
- Improve PostgreSQL loading and schemas while adding validation, tests, freshness checks and provider or LLM-based enrichment
What you bring
- At least five years building and running production data platforms across design, implementation, deployment and operations
- Hands-on production work with GCP, dbt and workflow orchestration, including dependency handling and recovery
- Strong Python skills for asynchronous HTTP and concurrent workloads, along with SQL and PostgreSQL expertise
- Experience with queues, data contracts, layered storage, retention, automated tests and pipeline monitoring
Who this fits
This senior role suits an engineer who can own data platform work from architecture through ongoing production operations. It is fully remote, but you must be based in Latin America; 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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