Data Engineer at Puma Energy

KEY RESPONSIBILTIES:

• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals.
• Develop complex queries and solutions using Scala, .NET, Python/PySpark languages.
• Implement and maintain data solutions on Azure Data Factory, Azure Data Lake, and Databricks
• Create data products for analytics and data scientist team members to improve their productivity.
• Advise, consult, mentor, and coach other data and analytic professionals on data standards and practices.
• Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytical solutions.
• Lead the evaluation, implementation, and deployment of emerging tools and processes for analytic data engineering in order to improve our productivity as a team.
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes.
• Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.
• Collaborate with other team members and effectively influence, direct, and monitor project work.
• Develop strong understanding of the business and support decision making.

REQUIREMENTS:

Experience:  

• 10 years of overall experience & at least 5 years of relevant experience  
• 5 years of experience working with Azure data factory & Databricks in a retail environment

Skills:  

• Bachelor's degree required; Computer Science Engineering.
• 5+ years of experience working in data engineering or architecture role.
• Expertise in SQL and data analysis and experience with at least one programming language (Scala and .NET preferred).
• Experience developing and maintaining data warehouses in big data solutions.
• Experience with, Azure Data Lake, Azure Data Factory, and Databricks) in the data and analytics space is a must
• Database development experience using Hadoop or Big Query and experience with a variety of relational, NoSQL, and cloud data lake technologies.
• Worked with BI tools such as Tableau, Power BI, Looker, Shiny.
• Conceptual knowledge of data and analytics, such as dimensional modelling, ETL, reporting tools, data governance, data warehousing, and structured and unstructured data.
• Big Data Development experience using Hive, Impala, Spark, and familiarity with Kafka.
• Exposure to machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics.