javapythonsqletldata modelingdata warehousingscalaeltdata qualitycloud data platforms
Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Работа на клиентовАктуальные технологииГибкие условияДежурства
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Текст:
Hirify AI/Оригинал
TL;DR
Senior Software Engineer - Data Engineering (ETL/ELT and cloud data platforms): Building and maintaining scalable data infrastructure, pipelines, and models that power analytics, reporting, and product features with an accent on data quality, reliability, and observability. Focus on designing production-grade data systems, diagnosing complex data issues, optimizing performance and cost, and supporting incident response.
Location: Remote 100%, Europe only; flexible working hours; no business travel required.
Company
hirify.global is a USA-based software engineering and solutions consulting company delivering high-impact development services to Fortune 500 companies.
What you will do
Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and deliver data from internal and external sources.
Develop and optimize data models and schemas for analytics, reporting, and downstream product applications.
Monitor pipeline health, troubleshoot data quality issues, and implement alerting and observability practices.
Partner with analytics, product, and engineering teams to translate data needs into durable solutions.
Improve the performance, scalability, and cost-efficiency of data infrastructure.
Document technical solutions, contribute to testing, code review, and deployment standards, and participate in on-call or incident response rotations as needed.
Requirements
4+ years of professional experience in data engineering or a closely related discipline.
Strong proficiency in SQL and at least one programming language commonly used for data engineering, such as Python, Scala, or Java.
Hands-on experience building and operating ETL/ELT pipelines in a production environment.
Experience with cloud data platforms such as AWS, GCP, or Azure.
Experience with modern data warehousing tools.
Solid understanding of data modeling, data quality, and pipeline orchestration concepts.
Ability to work with large, complex datasets and diagnose data issues independently.
Nice to have
Experience with orchestration tools such as Airflow, Dagster, or similar.
Familiarity with streaming data technologies such as Kafka or Kinesis.
Experience with dbt or similar transformation frameworks.
Exposure to infrastructure-as-code practices such as Terraform.
Prior experience supporting analytics or data science teams directly.
Culture & Benefits
Competitive compensation with benefits, paid vacation, and sick leave.
Globally diverse engineering team working on challenging engineering problems.
Remote-first and ultra-flexible working conditions with flexible working hours.
Generous home-office equipment allowance and access to a nearby office or coworking facility.
Start-up work environment with opportunities for professional growth and development.
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