Senior Data Engineer

Accountabilities: Own Data Platform Architecture: Design, build, and operate a production-grade data platform from initial architecture through deployment, optimization, and ongoing maintenance. Build Multi-Source Ingestion Pipelines: Develop concurrent ingestion systems for provider APIs, webhooks, files, and web scraping, incorporating rate limiting, error handling, and reliable processing at scale. Develop Batch and Event-Driven Workflows: Implement scheduled pipelines, historical backfills, and dependency management using orchestration tools such as Airflow, Dagster, or Prefect. Build event-driven processing with messaging and task queue services, including Pub/Sub or Cloud Tasks. Ensure Reliable Failure Recovery: Implement retries, dead-letter queues, controlled replay mechanisms, and idempotent upserts to recover from failures without generating duplicate records or corrupting data. Establish Raw-to-Curated Data Architecture: Preserve original source payloads, including provider JSON, in Google Cloud Storage (GCS), then transform and standardize data using dbt. Maintain clearly defined raw, standardized, and curated data layers. Strengthen Data Quality and Governance: Define data contracts, automated validation rules, pipeline tests, retention policies, and monitoring systems to track data freshness, processing failures, and overall reliability. Optimize Database Performance: Design and maintain PostgreSQL schemas, queries, and incremental loading strategies to ensure efficient processing and dependable data delivery to downstream products. Integrate Enrichment and AI Extraction: Incorporate third-party enrichment providers and large language model (LLM)-based structured extraction from text into the data platform. Ensure Data Traceability: Maintain clear lineage between source data and curated records, enabling safe reprocessing, historical recovery, auditing, and consistent data management. Drive Continuous Improvement: Identify opportunities to improve throughput, reliability, maintainability, and infrastructure efficiency while adapting the platform to evolving business and product needs. Requirements: 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. Benefits: Fully remote work: Work remotely from eligible locations across Europe and Latin America, with flexibility to organize your working environment. Technical ownership: Lead the architecture and evolution of a data platform, with meaningful influence over technical decisions and engineering practices. Modern technology stack: Work with Python, PostgreSQL, GCP, dbt, event-driven systems, workflow orchestration, web scraping, and AI-powered data extraction. End-to-end impact: Build foundational infrastructure that directly supports product functionality, data quality, and business growth. Engineering autonomy: Take ownership of technical challenges and shape scalable solutions in a dynamic, product-focused environment. Innovation opportunities: Explore advanced data processing, entity resolution, external data enrichment, and LLM-based extraction techniques. International collaboration: Work with a distributed team across multiple regions, with opportunities to contribute to evolving technical initiatives. Compensation flexibility: Salary expectations are discussed in USD on a monthly basis, with the final package determined through the recruitment process.