Senior Data Engineer

As a Senior Data Engineer, you will take ownership of the data platform, shaping its architecture and ensuring reliable performance from development through production. You will build scalable pipelines that transform high volumes of data from APIs, webhooks, files, and web scraping into trusted, product-ready information. A key priority will be evolving existing sequential workflows into a modern platform supporting concurrent processing, event-driven ingestion, and orchestrated batch execution. You will establish robust data quality standards, resilient recovery mechanisms, and transparent data lineage to ensure accuracy and consistency at scale. Working with technologies such as Python, PostgreSQL, dbt, and Google Cloud Platform, you will integrate external data sources and AI-powered extraction capabilities. This fully remote opportunity offers broad technical ownership, the freedom to shape foundational data infrastructure, and the chance to make a direct impact in a collaborative, fast-moving engineering environment.

• End-to-End Data Engineering Experience: Proven experience building production data platforms from the ground up, including architecture, pipeline implementation, deployment, monitoring, and ongoing operations.

• Workflow Orchestration: Hands-on production experience with Airflow, Dagster, Prefect, or Temporal, including workflow dependencies, retries, scheduling, and failure recovery. Airflow experience is preferred.

• Google Cloud and dbt: Practical production experience with Google Cloud Platform (GCP) and dbt, including data transformation, pipeline management, and cloud-based data infrastructure.

• Event-Driven Architecture: Experience developing asynchronous, event-driven systems using messaging platforms or task queues, with a strong understanding of concurrent processing, retries, and fault tolerance.

• Python Programming: Strong Python skills, particularly asynchronous HTTP requests and concurrent processing using libraries such as asyncio, aiohttp, or HTTPX.

• SQL and PostgreSQL: Advanced SQL knowledge and practical PostgreSQL experience, including data modeling, query optimization, incremental loading, and performance tuning.

• Data Quality and Modeling: Experience designing data contracts, raw and curated storage layers, retention policies, automated validation, and data quality monitoring.

• Web Scraping Expertise: Hands-on experience building and maintaining production scraping pipelines using tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrency management, rate limiting, and failure handling.

• Independent Ownership: Ability to make sound technical decisions, manage complex projects, and take responsibility for data infrastructure across the full development lifecycle.

• Startup or Small-Team Experience: Experience working in a startup or lean engineering team, where priorities evolve quickly and engineers take broad ownership, is a plus.

• Entity Resolution: Familiarity with matching company and individual records across multiple sources, identifying duplicates, and consolidating information into consistent entities is highly desirable.

• LLM-Based Data Processing: Experience using large language models for structured data extraction, company enrichment, or people data enrichment is an advantage.

• Additional Data Technologies: Familiarity with BigQuery and Dataform is a plus.

• Communication Skills: Ability to communicate technical concepts clearly, collaborate effectively with distributed teams, and work comfortably in an English-speaking professional environment.