Senior Data Engineer
New
C
CleraData infrastructure
Remote within the United States.Full-TimeSenior
Salary120,000 - 160,000 USD per year
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Job Details
- Experience
- At least 5 years building and operating production data platforms across multiple sources, from architecture through ongoing operations.
- Required Skills
- PostgreSQLPythonSQLGCPAirflowdbt
Requirements
- At least 5 years building and operating production data platforms across multiple sources, from architecture through ongoing operations.
- Production experience with workflow orchestration, including dependencies, retries, and failure recovery; Airflow is preferred, with Dagster, Prefect, Temporal, or comparable tools also relevant.
- Hands-on production experience with dbt and Google Cloud Platform.
- Strong Python skills, including asynchronous HTTP and concurrent processing with asyncio, aiohttp, HTTPX, or similar tools.
- Strong SQL and PostgreSQL skills, including data modeling, query optimization, and incremental loading.
- Experience with event-driven systems, messaging or task queues, asynchronous processing, and recovery from failures.
- Experience implementing data contracts, raw and curated storage, retention policies, automated validation, pipeline tests, and monitoring.
- Web scraping, entity resolution, data enrichment, BigQuery, or Dataform experience is helpful.
- Experience building platforms from scratch in a small team is helpful.
Responsibilities
- 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 Airflow, Dagster, Prefect, or similar tools.
- 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 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, automated 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.
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