Data Engineer

TTX Company•Charlotte, NC

7h•Hybrid

About The Position

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.

Requirements

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.
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.
Experience with various languages and advanced analytics tools such as SQL, Python, Power BI, Fabric, Microsoft SSIS/SSAS, Azure Synapse, and others.
Ability to design, build, and manage data structures and pipelines for encompassing data transformation, data models, schemas, metadata, and workload management. Work with both IT and business users, integrating analytics and data science output into business processes and workflows.
Experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architecture, and integrated datasets using data integration technologies, including ETL/ELT, data replication/CDC, message-oriented data movement, API design and development.

Nice To Haves

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

Responsibilities

Works in a hybrid cloud ecosystem, composed of Azure, Oracle, and on-premises technologies, building and supporting data and analytics solutions. Needs to learn the data, tools, and capabilities resident in this hybrid ecosystem, such as Fabric, Synapse, Data Lakes, Azure ML, and SQL Server.
Designs, builds, and maintains data pipelines in Azure and the on-premises ecosystems.
Assists with enhancing the data and metadata management infrastructure to ensure data quality, accessibility, and security.
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.
Researches and proposes data ingestion, preparation, integration, and operationalization tools or techniques to aid initiatives. Trains team members, data consumers, data scientists, and data analysts in these technologies and preparation techniques.
Works with data governance teams (and Data Stewards within these teams) in building, vetting, and promoting content, which adheres to data governance and compliance initiatives.
Promotes the available data and analytics capabilities and expertise to business unit leaders, educating them in leveraging these capabilities in achieving their business goals.