6 дней назад
Staff Data Quality Analyst
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
Staff Data Quality Analyst (Data Engineering): Designing and scaling data pipelines, data models, and storage solutions that power analytics, machine learning, and business intelligence with an accent on data quality, distributed systems, and maintainable platform infrastructure. Focus on building production ETL/ELT workflows, implementing observability and monitoring, and evolving data lakehouse architecture.
Location: Hyderabad, India; on-site
Company
provides software supply chain security solutions, including open-source dependency management, SBOM management, and protection against malicious open-source components.
What you will do
- Design, build, and maintain scalable data pipelines and ETL/ELT processes.
- Architect and optimize data models and storage solutions for analytics and operational use.
- Collaborate with data scientists, analysts, and engineers to deliver trusted datasets.
- Own and evolve data platform components such as Airflow, dbt, Spark, Redshift, or Snowflake.
- Implement observability, alerting, and data quality monitoring for critical pipelines.
- Contribute to data engineering standards, documentation, testing, CI/CD, and data lakehouse architecture.
Requirements
- 8+ years of experience as a Data Engineer or in a similar backend engineering role.
- Strong programming skills in Python, Scala, or Java.
- Hands-on experience with HBase or similar NoSQL columnar stores.
- Experience with distributed data systems such as Spark, Kafka, or Flink.
- Advanced SQL skills, including query performance optimization.
- Experience with production ETL/ELT pipelines, workflow orchestration, and data modeling techniques such as star schema and dimensional modeling.
Culture & Benefits
- Work on data problems supporting secure software development.
- Use open-source and cloud-native technologies.
- Collaborative environment emphasizing learning, autonomy, and impact.
- Parental leave and diversity and inclusion working groups.
- Flexible working practices.
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