Senior Data Engineer

About the Role

Join a data infrastructure and analytics team building a platform that turns information from multiple sources into reliable, current company intelligence. You will own the platform across architecture, ingestion, and production operations, supporting products with trusted data.

What You'll Do

• Design a multi-source data platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows.
• Build ingestion from provider APIs, webhooks, files, and web scraping, with rate limits and robust failure handling.
• Orchestrate scheduled workflows and historical backfills using Airflow, Dagster, Prefect, or similar tools.
• Develop event-driven processing with messaging or task queues, including retries, dead-letter handling, and controlled replay.
• Preserve source payloads in Google Cloud Storage and transform them with dbt into raw, standardized, and curated layers.
• Implement idempotent upserts and safe retries to prevent duplicate records and data corruption.
• Define data contracts, validation, pipeline tests, and monitoring for freshness and failures.
• 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 that integrate multiple sources, with ownership from architecture through deployment and ongoing operations.
• Production experience with workflow orchestration, including dependencies, retries, and recovery; Airflow is preferred.
• 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 skills, including data modeling, query optimization, and incremental loading.
• Experience with event-driven systems, messaging or task queues, data contracts, raw and curated storage, retention policies, automated testing, and pipeline monitoring.
• Experience building platforms or substantial pipelines from scratch is valuable. Web scraping, entity resolution, BigQuery, Dataform, and LLM-based extraction are also relevant.