Senior Data Engineer

Job description

The Role: We’re looking for a senior data engineer to own our data platform from architecture through production, reliably turning high volumes of data from provider APIs, webhooks, files, and web scraping into trusted records for our products. Today, our enrichment and extraction workflows largely run serially inside a container. You’ll lead the move to a platform that supports concurrent processing, event-driven intake, and orchestrated batch workflows. Responsibilities: Multi-source intake: Build concurrent ingestion for provider APIs, webhooks, files, and scraping, with rate limits and failure handling. Batch and event-driven execution: Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect. Build event-driven intake with Pub/Sub or Cloud Tasks, including retries, dead-letter queues, and controlled replay of failed work. Raw-to-curated data: Preserve original payloads, including provider JSON, in Google Cloud Storage (GCS) before transforming them with dbt. Establish medallion layers for raw, standardized, and curated data. Correctness through recovery: Implement idempotent upserts and safe retries, replay, and historical backfills that avoid duplicate records or data corruption. Make data traceable to its source and safe to reprocess. Data quality and delivery: Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures. Design and optimize PostgreSQL schemas, queries, and loading patterns so downstream products receive dependable data. Extraction and enrichment: Integrate provider enrichment and LLM-based structured extraction from text into the platform. Requirements: Experience building multi-source production data platforms from zero to one, from initial architecture and pipeline implementation through deployment and ongoing operation. Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal, including dependencies, retries, and failure recovery. Airflow experience is preferred. Hands-on production experience with dbt and GCP. Experience building event-driven systems with messaging or task queues, including asynchronous processing and failure recovery. Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading. Experience with data contracts, raw and curated storage, retention policies, and automated quality checks. Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX. Hands-on web scraping experience with tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrent scraping, rate limits, and failure handling. Nice-to-Have: Experience working in startups or small engineering teams, with broad ownership and changing priorities. Entity resolution experience is especially valuable: matching company and people records across sources and reconciling duplicates into consistent entities. LLM-based structured extraction or company and people data enrichment. Familiarity with BigQuery and Dataform.