Senior Data Engineer

Summary

Join Nintex as a Data Engineer and contribute to the development of high-quality data engineering products and services. You will be a technical leader, ensuring solutions meet team requirements and leveraging cutting-edge cloud technologies. This one-year contract role involves designing, implementing, and testing data engineering components, collaborating with data analysts and scientists, and contributing to organizational data standards. You will utilize various data pipeline architectures and advise on distributed storage and computing technologies. The ideal candidate possesses extensive experience in data engineering, cloud platforms, and distributed data frameworks.

Requirements

• 5+ years experience in data engineering
• Cloud Platform, i.e. AWS / Azure / GCP
• Cloud DBMS, i.e. Databricks / Snowflake / Amazon Redshift / BigQuery
• Distributed Data frameworks, i.e. Hadoop / Spark / Flink
• Programming, i.e. Java / Scala / Python
• SQL, any variant is a good foundation but HiveQL / Spark SQL preferred
• Database modeling, i.e. Snowflake Schema / Star Schema / Data Vault

Responsibilities

• Identify and pursue opportunities to create or improve organizational data standards
• Design and implement data engineering solutions that adhere to Nintex guidelines in support of security, disaster recovery, scalability, availability, reliability, and durability
• Design, code, and unit test data engineering components and features that can be released / deployed through automation
• Make informed recommendations about the data engineering technology appropriate for implementing a solution
• Improve data quality through testing, tooling and continuous evaluation of the data engineering process and product
• Work collaboratively with data analysts, data scientists to create valuable insights from complex data
• Contribute to the support and maintenance of products or services, including liaising with the support team to resolve issues that have been escalated to the product team
• Apply different data pipeline architectures appropriately to meet product requirements (data warehouse, data lake, data lakehouse, etc.)
• Advise on the implications of different distributed storage and computing technologies when applied to the complex data engineering requirements
• Contribute to the hygiene processes to improve and harden data delivery within the team
• Be an active part of the incident management process, including on-call rotation and unblocking technical and operational decisions
• Work with data providers to document the incoming data stream’s domain boundaries, field datatypes, and term definitions in sufficient detail to enable transformation of the data for end-user consumption
• Contribute to organizational data standards and architectural patterns and practices for the data domain

Preferred Qualifications

• Streaming and event-driven systems, i.e. Apache Kafka / Azure EventHubs / Spark Structured Streaming
• Data testing, i.e. unit tests and quality enforcement
• DevOps platform, i.e. Azure DevOps / GitHub
• Scripting, i.e. Bash / Powershell
• Infrastructure as code, i.e. Terraform / ARM templates / AWS Cloudformation

Benefits

• Global Gratitude and Recharge Days
• Flexible, paid time off policy
• Employee wellness programs and counseling resources
• Meaningful peer recognition and awards
• Paid parental leave
• Invention/patenting assistance
• Community impact, paid volunteer time, and opportunities
• Intercultural learning and celebration
• Multiple tools through which to learn and grow, and an incredible global community
• Our people work in the way that best suits them and their teams - whether at home, in an office, or another place that sparks creativity, focus, and collaboration. Our work environment is such that our people can successfully deliver their work while adequately supporting their lifestyle and preferences