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

PostedNov 12, 2026

Role brief

remoteroles summary, based on the employer's posting.

What you'll do

Build a platform that pulls from multiple sources, handles concurrent intake, processes events and runs scheduled batch jobs
Bring in API, webhook, file and scraped data, managing rate limits and recovering from failed requests
Set up scheduled runs and historical backfills, then build queue workflows with retries, dead-letter handling and replay controls
Keep source payloads in cloud storage, use dbt to shape data into layers, and add contracts, tests and quality monitoring
Design PostgreSQL schemas and loading patterns, while supporting enrichment and structured extraction with LLMs

What you bring

At least five years building and operating production data platforms from initial design through deployment and ongoing support
Production work with Google Cloud Platform, dbt and a workflow orchestrator such as Airflow, Dagster or Prefect
Strong Python skills for asynchronous HTTP and concurrent processing, plus solid SQL and PostgreSQL knowledge
Experience with event-driven processing, queues, recovery from failures, data contracts, automated tests and data quality monitoring

Who this fits

This senior role suits someone who has owned production data platforms across architecture, deployment and operations. It is remote for candidates based in Latin America. Visa sponsorship is unavailable.

From the employer

About the Role

Join a data and 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 to production operations, helping provide trusted data for downstream products.

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 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.
Build messaging and task queue workflows with retries, dead-letter queues, and controlled replay.
Store original payloads in cloud object 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, with ownership from architecture through deployment and ongoing operations.
Production experience with Google Cloud Platform, dbt, and workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal.
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 incremental loading.
Experience with event-driven systems, messaging or task queues, asynchronous processing, and failure recovery.
Experience with data contracts, raw and curated storage, retention policies, automated testing, and data quality monitoring.
Startup or small-team experience is valuable. Web scraping, entity resolution, data enrichment, and LLM-based extraction are also useful.