обновлено 12 часов назад
Analyst Software Engineering (Data Engineering)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Analyst Software Engineering (Data Engineering): Building and supporting batch and streaming data pipelines and curated datasets on a modern data platform with an accent on data modelling, quality controls, distributed processing, and production reliability. Focus on designing scalable pipelines, reconciling data outputs, optimizing performance, and contributing to reusable platform tooling.
Location: Dallas, TX, United States; office location: Dallas
Company
operates engineering teams that build scalable systems and data platforms supporting analytics, operational decision-making, and AI use cases.
What you will do
- Build, enhance, and support batch and streaming data pipelines on the Lakehouse and AI data platform.
- Develop raw, refined, and curated datasets for analytics, reporting, and AI use cases.
- Apply data modelling principles covering business entities, relationships, historical changes, schema evolution, and compatibility.
- Implement data quality, validation, monitoring, and reconciliation controls for production datasets.
- Optimize distributed data processing and modernize existing data flows for reliability, performance, and maintainability.
- Collaborate with engineers, platform teams, and data consumers while contributing to reusable tooling and shared platform components.
Requirements
- Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with strong quantitative or data engineering skills.
- Hands-on programming experience in Python or Java.
- Working knowledge of SQL, including troubleshooting, optimization, and data analysis.
- Experience with production data pipelines and distributed data processing frameworks such as Apache Spark.
- Understanding of software engineering fundamentals, including version control, testing, release discipline, and CI/CD.
- Knowledge of data formats such as JSON, Avro, and Parquet, plus partitioning, clustering, data quality, and root-cause analysis.
Nice to have
- Experience with Kafka, Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, or Sybase IQ.
- Experience with containerized or Kubernetes-based deployment approaches.
- Ability to guide technical design, implementation standards, delivery workstreams, and less experienced engineers.
Culture & Benefits
- Fast-paced engineering environment focused on reliable production solutions.
- Opportunities to develop long-term expertise in data engineering and platform technologies.
- Close partnership with stakeholders, platform teams, and data consumers.
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