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 · $100,000–$140,000 base.

SalaryStated by Clera

$100,000 – $140,000

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

RoleData Engineering

SenioritySenior

LocationWorldwide

WorkplaceRemote

EmploymentFull-time

Base pay$100,000–$140,000

PostedNov 5, 2026

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Build a platform that takes in data from several sources through parallel ingestion, event processing and scheduled batch workflows
Collect data through provider APIs, webhooks, files and scraping, with rate controls and recovery when requests fail
Run scheduled pipelines and historical reloads, using orchestration tools such as Airflow, Dagster or Prefect
Keep source payloads in Google Cloud Storage, then use dbt to create raw, standardized and curated data layers
Add safe repeatable loads, data checks, pipeline tests and health monitoring while optimizing PostgreSQL and integrating enrichment and LLM extraction

What you bring

At least five years building and running production data platforms, from initial design through deployment and ongoing support
Production experience with workflow orchestration and recovery; hands-on use of dbt and Google Cloud Platform
Strong Python skills for asynchronous and concurrent processing, plus SQL and PostgreSQL experience in modeling and incremental loads
Experience with event-driven queues, failure recovery, data contracts, validation, pipeline testing and monitoring

Who this fits

This suits a senior engineer who has built and operated production data platforms and can own work from architecture through operations. The role is remote within Europe, including the United Kingdom, Norway, Switzerland, Iceland, Liechtenstein and Ukraine. 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 to production operations, enabling 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 provider 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 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, 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, integrating multiple sources from architecture through deployment and ongoing operations.
Production experience with workflow orchestration, including dependencies, retries, and failure recovery; Airflow experience is preferred.
Hands-on production experience with dbt and Google Cloud Platform.
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, and asynchronous failure recovery.
Experience with data contracts, raw and curated storage, retention policies, automated validation, pipeline testing, and monitoring.
Experience building platforms or substantial pipelines from scratch is valuable. Web scraping, entity resolution, LLM-based extraction, BigQuery, and Dataform experience are also useful.