Senior Data Engineer

Clera getclera.com Open · verified Oct 24, 2026

Remote Worldwide Full-time Senior Data Engineering

At a glance

Senior Data Engineering role at Clera. Remote · full-time · $120,000–$160,000 base.

SalaryStated by Clera

$120,000 – $160,000

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

RoleData Engineering

SenioritySenior

LocationWorldwide

WorkplaceRemote

EmploymentFull-time

Base pay$120,000–$160,000

PostedOct 20, 2026 · 4d ago

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Create a shared data system for concurrent source intake, event processing and scheduled batch work
Handle API, webhook, file and scraped inputs, accounting for provider limits and failed requests
Run scheduled pipelines and historical reloads, with queue retries, dead-letter handling and controlled replay
Keep original records in Google Cloud Storage, then use dbt to shape raw, standardized and curated datasets
Add contracts, automated checks, pipeline tests and failure alerts, while tuning PostgreSQL for downstream workloads

What you bring

At least five years building and running production data platforms, including architecture, launch and ongoing support
Production use of an orchestration tool such as Airflow, Dagster or Prefect, plus dbt and Google Cloud Platform
Strong Python for asynchronous HTTP and concurrent processing, along with SQL and PostgreSQL for modeling and incremental loads
Experience with event-driven systems and implementing data contracts, retention, validation, tests and monitoring

Who this fits

This remote role is limited to people based in the United States, and visa sponsorship is unavailable. It suits a senior engineer ready to own platform work end to end and adapt to changing priorities on a small team.

From the employer

About the Role

Join an early-stage data infrastructure company building a platform that transforms information from multiple sources into reliable, current company intelligence. You will own the data platform end to end, from architecture and ingestion through production operations, helping ensure trusted data powers downstream products.

What You'll Do

Design and build a multi-source platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows.
Develop resilient intake from provider APIs, webhooks, files, and web scraping, including rate limits and failure handling.
Orchestrate scheduled workflows and historical backfills with tools such as Airflow, Dagster, or Prefect.
Build event-driven processing with messaging or task queues, including retries, dead-letter queues, and controlled replay.
Store original payloads in Google Cloud Storage and use dbt to transform data 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 for downstream use.
Integrate provider enrichment and LLM-based structured extraction into the platform.

What We're Looking For

At least 5 years building and operating production data platforms, with ownership from architecture through deployment and ongoing support; 8 or more years is a plus.
Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal, including dependencies, retries, and recovery.
Hands-on production experience with dbt and Google Cloud Platform.
Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
Strong SQL and PostgreSQL experience in data modeling, query optimization, and incremental loading.
Experience building event-driven systems and implementing data contracts, retention policies, validation, testing, and monitoring.
Web scraping, entity resolution, data enrichment, and LLM-based extraction experience are helpful.
Comfort working with broad ownership and changing priorities in a small team; BigQuery and Dataform familiarity is a plus.