Senior Data Engineer, LATAM

Clera getclera.com Open

Remote Worldwide Full-time Senior Data Engineering

At a glance

Senior Data Engineering role at Clera. Remote · full-time · $80,000–$120,000 base.

SalaryStated by Clera

$80,000 – $120,000

Base salary for this role, in USD per year, as published in the posting.

RoleData Engineering

SenioritySenior

LocationWorldwide

WorkplaceRemote

EmploymentFull-time

Base pay$80,000–$120,000

PostedNov 5, 2026

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Design a platform that ingests multiple sources concurrently and supports event-based processing and scheduled batch jobs
Build API, webhook, file and scraping pipelines with rate controls and recovery from failures
Set up workflow scheduling, historical backfills, and queue handling with retries and replay controls
Store original payloads in Google Cloud Storage, then use dbt to shape raw, standardized and curated data
Improve PostgreSQL loading and schemas, adding data checks, pipeline tests and monitoring for freshness and failures

What you bring

At least five years building and running production data platforms that combine multiple sources, from design through operations
Production use of dbt and Google Cloud Platform, plus an orchestration tool such as Airflow or a comparable option
Strong Python and SQL skills, including asynchronous processing, PostgreSQL modeling and dependable incremental loads
Experience building event-driven queues and recovery, along with data contracts, testing and production monitoring

Who this fits

This senior, hands-on role suits someone who can own data platforms from architecture through ongoing operations. It is fully remote for candidates based in Latin America; visa sponsorship is unavailable.

From the employer

About the Role

Join a data infrastructure and analytics team building a platform that turns information from multiple sources into reliable, current company intelligence. You will own the platform end-to-end, from architecture and ingestion through production operations.

What You'll Do

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 robust 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, and integrate enrichment and LLM-based structured extraction.

What We're Looking For

At least 5 years building and operating production data platforms across multiple sources, with ownership from architecture through deployment and ongoing operations.
Hands-on production experience with dbt and Google Cloud Platform, and workflow orchestration using Airflow, Dagster, Prefect, Temporal, or a comparable tool. Airflow is preferred.
Strong Python skills, including asynchronous HTTP and concurrent processing with asyncio, aiohttp, or HTTPX.
Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading.
Experience building event-driven systems with messaging or task queues, asynchronous processing, and failure recovery.
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, LLM-based extraction, and building platforms from scratch in small teams.