Senior Data Engineer

Clera · posted today ago

engineering python data machine-learning

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About this role

About the Role

Join a data infrastructure and analytics team building a platform that turns information from many sources into reliable, current company intelligence. You will own the platform end to end, from architecture and ingestion through production operations, helping power downstream 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 using tools such as Airflow, Dagster, or Prefect.
Build messaging and task queue workflows with 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.
Develop idempotent upserts, data contracts, automated quality checks, pipeline tests, and monitoring for freshness and failures.
Design and optimize PostgreSQL schemas and loading patterns for reliable downstream use.
Integrate data enrichment and LLM-based structured extraction into the platform.

What We're Looking For

5 to 10 or more years of experience, including production ownership of multi-source data platforms from architecture through deployment and ongoing operation.
Strong hands-on experience with Airflow or similar orchestration tools, dbt, and Google Cloud Platform.
Experience building event-driven systems with messaging or task queues and asynchronous processing.
Strong SQL and PostgreSQL skills, including data modeling, query optimization, and incremental loading.
Proficiency in Python and concurrent or asynchronous processing, such as asyncio, aiohttp, or HTTPX.
Practical web scraping experience with tools such as Scrapy, Playwright, or Selenium.
Experience with data contracts, storage layers, and automated data quality checks. Entity resolution is valuable; BigQuery, Dataform, and LLM-based structured extraction are helpful.
Comfort working in a startup or small team with broad ownership and adaptability.