Data Engineer

Data Engineer

One of the Select Group’s clients is seeking a Data Engineer to join their team and develop short and long term solutions to prepare data (gather, integrate, clean) for threat evaluations and risk analytics. The ideal candidate has strong experience with Python, SQL scripting, big data toolsets, proficient knowledge in data modeling and data architecture, and experience in the Oil & Gas industry (pipeline integrity specifically). This role can sit remote in the USA (must work PDT/PST work hours). We are unable to provide sponsorship or work c2c through an employer for this opportunity.

Data Engineer Requirements:

• 4+ years of experience in Data Engineering or similar
• Experienced with Data Integration and Data Integration tools (SAP Data Services, Informatica, MS SSIS, etc)
• Proficient knowledge of data modeling and data architecture
• Experience with Big Data and Big Data toolsets (like Hadoop, Spark, Kafka, etc)
• Proficient with Python and SQL Scripting
• Advanced Excel skills and proficiency with PowerBI
• Some background and skills in Data Science and quantitative analysis
• Oil & Gas (pipeline integrity) knowledge is highly desire

Data Engineer Responsibilities:

• Works with multiple business subject areas/data domains, and applies expertise in multiple technology stacks and data services capabilities (integration, storage/processing, visualization).
• Extract data from different sources (SAP Hana, AWS cloud, internal/ external sources) and transform it into a suitable format for query and analysis.
• Prepares datasets, tools and documentation to enable Risk & Threat engineers to perform threat evaluations, risk analytics and reports.
• Design, build and implement applications for visualizing data and risk results to enable action (e.g., data improvements, P&M activities).
• Develop & continuously improve business knowledge and relationships to integrate activities and data with other BU’s (EAM, GIS, HPPD, IM) to ensure successful implementation and support Threat & Risk priorities.
• Identify, design, and implement internal process improvements such as automating manual processes, optimizing data delivery, etc.
• Establishes and executes data quality management.
• Develops roadmap to data-flow automation, granular data, and maturity path to more frequent and quantitative assessments.