Senior Staff Data Engineer

Summary

Join Narvar's Data Engineering team as a Sr. Staff Data Engineer to lead, design, and build data pipelines and systems for processing large datasets. You will develop and implement data pipelines using technologies like Spark, Airflow, and BigQuery, collaborating with data scientists and other teams. Responsibilities include maintaining and optimizing existing systems, improving data quality, and staying current with advancements in data engineering. This role significantly impacts Narvar's business, partners, and millions of consumers globally. Narvar handles transactional data for over 1500 brands and retailers, transforming post-purchase experiences. The ideal candidate possesses extensive experience in data engineering, proficiency in various programming languages, and expertise in big data processing systems and cloud technologies.

Requirements

• Bachelors in Computer Science, Engineering
• You have 15 - 20+ years of relevant experience
• Proficiency with Java, Golang, Scala, or Python
• Flexibility to use and learn new languages and technologies
• Strong knowledge of computer science fundamentals and data structures
• Expert SQL skills
• Hands-on experience building big data processing systems
• Experience with Cloud technology stacks (e.g., GCP or AWS and their product offerings)
• You have dealt with large amounts of data in production and have built distributed data processing using frameworks like Spark, Hadoop, Apache Beam, or Flink
• Experience with large-scale data warehousing architecture, data lakes, and data modeling
• Experience with Data Ops and data reliability
• Experience with error handling, data validation, dbt models

Responsibilities

• Develop and implement data pipelines and systems that can handle large volumes of data
• Process TBs of data delivering actionable insights and intelligence using technologies such as Spark, Airflow, Google Pubsub, Pulsar, BigQuery, DBT
• Collaborate with data scientists and other teams to integrate data into business processes and decision making
• Maintain and optimize existing data systems for costs, ease of access, and data governance
• Improve data quality by building any tooling, testing, and observability pipelines
• Stay up to date with the latest advances in data engineering and implement new technologies as needed

Preferred Qualifications

Previous startup experience