Senior Data Engineer

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

PostedOct 28, 2026

Role brief

remoteroles summary, based on the employer's posting.

What you'll do

Build a multi-source platform that handles concurrent arrivals, event processing and scheduled batch work
Collect provider data through APIs, webhooks, files and scraping, managing rate limits and failed requests
Run scheduled pipelines and historical backfills, and manage queues with retries, dead-letter handling and replay
Store original records in cloud storage, then use dbt to create raw, standardized and curated data layers
Improve PostgreSQL schemas and loading, and connect enrichment services with structured extraction using LLMs

What you bring

At least five years developing and running live data platforms that combine information from multiple sources
Production use of dbt and Google Cloud Platform, plus a workflow orchestrator such as Airflow or Dagster
Strong Python skills for asynchronous requests and concurrent work, alongside solid SQL and PostgreSQL ability
Experience creating event-based processing with queues, asynchronous tasks and recovery from errors

Who this fits

This senior individual contributor role suits someone ready to own data systems from initial design through deployment and ongoing operations. It is remote, but you must live 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, up-to-date company intelligence. You will own the platform end to end, from architecture and ingestion through production operations, supporting 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.
Ingest data from provider APIs, webhooks, files, and web scraping, with rate limiting and robust failure handling.
Orchestrate scheduled workflows and historical backfills with tools such as Airflow, Dagster, or Prefect.
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, and integrate enrichment providers and LLM-based structured extraction.

What We're Looking For

At least 5 years building and operating production data platforms that integrate multiple sources, with ownership from architecture through deployment and ongoing operations.
Production experience with dbt and Google Cloud Platform, plus workflow orchestration using Airflow, Dagster, Prefect, Temporal, or a comparable tool. Airflow experience is preferred.
Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as 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 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 also useful.