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Job Purpose and Impact

The Data Engineering Senior Supervisor will lead a small team of data engineering professionals to design, build and operate high performance data centric products or solutions through the use of modern engineering practices and technologies. In this role, you will provide guidance to ensure the delivered data centric products or solutions are scalable, sustainable and robust. You will also lead team development and cross team relationships and delivery to advance the company's engineering delivery.

Key Accountabilities

• Lead a local or regional team of engineering professionals that design, develop, deploy and enhance new and existing data centric products or solutions through the use of modern platforms and technologies.
• Provide routine inputs and guidance to the data engineering team on business requirements analysis, technical specification's preparation, product delivery, testing and operation to support businesses strategic direction.
• Coach engineers, share relevant technical approaches, identify new trends, modern skills and present new methodologies within the data engineering community.
• Lead and develop a team, coach and make decisions related to talent management, hiring, performance, and disciplinary actions.
• Other duties as assigned

Qualifications

Minimum Qualifications

• Bachelor's degree in a related field or equivalent experience
• Minimum of four years of related work experience
• Minimum of two years leading a team of Data Engineers

Preferred Qualifications

• Confirmed data engineering experience with data storage and management of large, heterogenous datasets, including formats, structures, and cataloging with such tools as Iceberg, Parquet, Avro, ORC, S3, HFDS, HIVE, Kudu or others.
• Experience leading Big Data engineering teams working in environments such as Hadoop and Spark
• Experience leading technology strategies with cloud Platforms including AWS, GCP or Azure
• Experience leading engineering teams working in streaming and stream integration or middleware platforms, tools, and architectures such as Kafka, Flink, JMS, or Kinesis.
• Confirmed knowledge of best practice patterns and how to apply them to modern data architectures, including data warehouses, data lakes, data mesh, hubs and associated capabilities including ingestion, governance, modeling, observability and more.
• Experience leveraging transformation and modeling tools, including SQL based transformation frameworks, orchestration and quality frameworks including dbt, Apache Nifi, Talend, AWS Glue, Airflow, Dagster, Great Expectations, Oozie and others
• Strong programming knowledge of SQL, Python, R, Java, Scala or equivalent
• Proficiency in engineering tooling including docker, git, and container orchestration services
• Strong experience leading devops teams with demonstratable understanding of associated best practices for code management, continuous integration, and deployment strategies.
• Experience and knowledge of data governance considerations including quality, privacy, security associated implications for data product development and consumption.
• A passion for quality and continuous improvement with an owners mindset
• Ability to work effectively as part of a team, group and culture
• Ability to navigate ambiguity and work in agile ways

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