Senior Data Engineer

Job not on LinkedIn

🔥 13 hours ago

🇪🇺 Europe – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

NIR-YU

WebsiteLinkedInAll Job Openings

201 - 500 employees

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

NIR-YU is a company dedicated to empowering small and medium enterprises (SMEs) by providing tailored nearshore staffing solutions. They focus on strategic recruitment of skilled nearshore professionals, offering cost-plus pricing for transparency and affordability. Their services include staff augmentation, staff leasing, talent acquisition, and employer of record (EoR), aimed at helping SMEs gain access to a skilled workforce from Latin America. By facilitating the hiring of remote talent and ensuring compliance with local regulations, NIR-YU allows businesses to enhance their teams with English-speaking professionals, optimize costs, and focus on growth strategies without the burden of setting up foreign entities.

📋 Description

• Build concurrent ingestion for provider APIs, webhooks, files, and web scraping, with rate limits and failure handling • Orchestrate scheduled workflows and historical backfills using 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 • Preserve original payloads, including provider JSON, in Google Cloud Storage before transforming them with dbt • Establish medallion layers for raw, standardized, and curated data • Implement idempotent upserts, safe retries, replay, and historical backfills that avoid duplicate records or data corruption • Make data traceable to its source and safe to reprocess • Define data contracts, automated validation, pipeline tests, freshness monitoring, and failure monitoring • Design and optimize PostgreSQL schemas, queries, and loading patterns • 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 • 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 • Airflow experience preferred • Experience working in startups or small engineering teams (nice-to-have) • Entity resolution experience (nice-to-have) • LLM-based structured extraction or company and people data enrichment (nice-to-have) • Familiarity with BigQuery and Dataform (nice-to-have)

🏖️ Benefits

• Fully remote work arrangement • Opportunity to own the data platform from architecture through production • Broad ownership and changing priorities in startup or small-team environments (nice-to-have context)