Data Engineer

POSITION SUMMARY

The Data Engineer is an interdisciplinary individual on the Data Analytics team who collaborates closely with multiple stakeholders across the enterprise and Information Technology (IT), to ensure the most important data is accessible and well-understood.

A Data Engineer designs, develops, implements, and supports new and existing highly efficient ELT/ETL processes and data sets. Other responsibilities include working closely with data consumers, solution architects, security, and governance teams to implement solutions to answer complex questions and drive business decisions. Apply your proven communication skills, problem-solving skills, and knowledge of best practices in designing, developing, and deploying data and analytic solutions.

Data Engineers need to be adept in several technical and business skills. These include working with diverse datasets, parsing and understanding data, working with domain experts, data scientists, and analysts in framing business problems, and provisioning integrated data quickly across multiple environments. Data Engineers should be inquisitive and motivated to learn modern technologies and capabilities that benefit the organization, and lead the effort in evaluating technology for acceptance at TTX. Data Engineers also assist the business data science efforts with source data, building data sets, helping evaluate models, and integrating analytics and data science model outputs into business processes.

KEY RESPONSIBILITIES

·         Hybrid cloud environment: the Data Engineer works in a hybrid cloud ecosystem, composed of Azure, Oracle, and on-premises technologies, building and supporting data and analytics solutions. The Data Engineer will need to learn the data, tools, and capabilities resident in this hybrid ecosystem, such as Fabric, Synapse, Data Lakes, Azure ML, and SQL Server.

·         Build data pipelines: Managed data pipelines consist of a series of stages through which data flows. Designing, building, and maintaining data pipelines in Azure and the on-premises ecosystems will be the primary responsibility of the data engineer.

·         Drive data-centric decision making. Assists with enhancing the data and metadata management infrastructure to ensure data quality, accessibility, and security.

·         Collaborate across departments: Collaborates with business data consumers of various skill levels in refining their requirements for various data and analytics initiatives. This collaboration can lead to building enterprise data products, enabling data-driven decision-making.

·         Lead, educate, and train: Be curious and knowledgeable about innovative technologies and data initiatives. Research and propose data ingestion, preparation, integration, and operationalization tools or techniques to aid these initiatives. Train team members, data consumers, data scientists, and data analysts in these technologies and preparation techniques.

·         Participate in ensuring compliance and governance during data use: Data Engineers work with data governance teams (and Data Stewards within these teams) in building, vetting, and promoting content, which adheres to data governance and compliance initiatives.

·         Become a data and analytics evangelist: TTX considers the Data Engineer a blend of data and analytics “evangelist,” “guru” and “fixer.” This role promotes the available data and analytics capabilities and expertise to business unit leaders, educating them in leveraging these capabilities in achieving their business goals.

CORE COMPETENCIES

• Education and Training

·         A bachelor's degree in computer science, statistics, applied mathematics, data management, information systems, information science, or a related quantitative field [or equivalent work experience] is required.

·         The ideal candidate will have a combination of IT, data governance, analytics, and communication skills.

Previous Experience

·         At least 2 years or more of work experience in data management disciplines, including data integration, modeling, optimization, data quality, and/or other areas directly relevant to data engineering responsibilities and tasks.

·         At least 2 years of experience working in cross-functional teams and collaborating with business stakeholders in support of a departmental and/or multi-departmental data management and analytics initiative.

Technical [and Business] Knowledg