Senior Data Engineer

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.