Staff Data Engineer

What you'll do...

Position: Staff Data Engineer

Job Location: 702 SW 8th St, Bentonville, AR 72716

Duties: Problem Formulation: Translates business problems within one's discipline to data related or mathematical solutions. Identifies what methods (for example, analytics, big data analytics, automation) would provide a solution for the problem. Shares use cases and gives examples to demonstrate how the method would solve the business problem. Applied Business Acumen: Provides recommendations to business stakeholders to solve complex business issues. Develops business cases for projects with a projected return on investment or cost savings. Translates business requirements into projects, activities, and tasks and aligns to overall business strategy. Serves as an interpreter and conduit to connect business needs with tangible solutions and results. Recommends new processes and ways of working. Data Governance: Establishes, modifies, and documents data governance projects and recommendations. Implements data governance practices in partnership with business stakeholders and peers. Interprets company and regulatory policies on data. Educates others on data governance processes, practices, policies, and guidelines. Provides recommendations on needed updates or inputs into data governance policies, practices, or guidelines. Data Source Identification: Understands the priority order of requirements and service level agreements. Defines and identifies the most suitable sources for required data that is fit for purpose, referring to external sources as required. Performs initial data quality checks on the extracted data. Data Transformation and Integration: Builds the infrastructure required for optimal transformation and integration from a wide variety of data sources using appropriate data integration technologies. Uses modern tools, techniques, and architectures to partially or completely automate the most common, repeatable and tedious data preparation and integration tasks. Deploys pipelines using scheduling and orchestration frameworks. Evaluates impacts of data issues and risks at an early stage. Identifies needs and creates methods to fuse and reshape complex, multi-source data and make it usable for modeling. Updates knowledge of current and emerging big data analytics and data science trends and techniques. Data Modeling: Assembles large, complex data across all data platforms (for example, relational, dimensional, NoSQL) and data tools. Builds complex logical and conceptual models and provides guidance to team on physical data models. Identifies and defines the appropriate techniques for exposing data to other systems. Reviews and provides guidance and inputs on all data modeling activities to team members. Creates and maintains critical data documentation and metadata that allows data to be understood and leveraged as a shared asset. Assists in defining data modeling standards and foundational best practices. Provides inputs to the architectural design to make best use of the available resources, given goals, and expected loads. Code Development and Testing: Reviews the solution and application design to ensure it meets business, technical, and data requirements. Identifies language and libraries to use in the development process. Maps test cases to business and functional requirements. Creates proof of concepts. Reviews and troubleshoots code in line with final designs. Identifies and recommends the appropriate testing methodology. Identifies the environment(s) for deployment. Identifies and recommends modifications of application based on different environment requirements. Identifies modifications needed for scalability and drives the change. Monitors applications in production and leads development of patches where required. Reviews and ensures all code documentation is complete and updated periodically. Data Strategy: Understands, articulates, interprets, and applies the principles of the defined strategy to unique, moderately complex business problems that may span one or main functions or domains.        

Minimum education and experience required: Master's degree or the equivalent in Computer Science, Information Technology, Engineering, or a related field plus 2 years of experience in software engineering or related experience; OR Bachelor's degree or the equivalent in Computer Science, Information Technology, Engineering, or a related field plus 4 years of experience in software engineering or related experience.

Skills required: Must have experience with: Coding in an object-oriented programming language (C#, Python, R, Jupyter notebooks), through the usage of Vertex AI or similar; Data Architecture skills and writing technical documentations and specs to lead data analytics requirements using tools like Visio, Draw.io and/or Erwin data modeler; Analyzing and testing Relational Database Management Systems (MSSQL Server, Oracle DB, DB2, Teradata, informix), DBeaver usage as a SQL client software; Understanding how to navigate OLTP databases like Microsoft SQL Server, Oracle DB, for finding errors in the data and lead data quality issue remediation; Producing insights using SQL scripts and queries in large scale enterprise data lake technologies and architectures (Google Cloud Platform, BigQuery, Hive, Presto/Trino, Azure); Delivering analytics reports and dashboards using visualization tools like Microsoft Excel, Tableau, R Data Visualization packages, Power BI and Looker; Creating automated data pipelines using tools (SSIS, Alteryx, PySpark, Airflow, Google Data Transfer services); Providing consultative guidance on data management for multiple data pillars including supply chain, store operations and transportation and contribute to the organizational efficiency; Working with tech partners and engineering team on executing the Software Development Life Cycle (SDLC) across multiple data lake projects and its versioning control using GitHub, including bug tracking, feature requests, task management, continuous integration; Overseeing Change Management and driving customer success using User data adoption tools and statistics framework; Working with the security tech partners on establishing the corresponding Solution Security Plan (SSP); Possessing expertise in data management across various data platforms, providing consultative guidance on data migrations from multiple data sources; and Designing and conducting technical trainings on Big Data platforms, Data Science technologies and best industry practices. Employer will accept any amount of experience with the required skills.

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Wal-Mart is an Equal Opportunity Employer.