DIRECTOR, DATA ENGINEERING

What youll do...

Tech Problem Formulation

• Requires knowledge of: Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights
• To analyze the business problem within ones discipline and questions assumptions to help the business identify the root cause
• Identifies and recommends approach to resolve the business problem to create effective technology focused solutions
• Sets relevant deliverables based on the established success criteria and define key metrics to measure progress and effectiveness of the solution

Quantifies business impact Understanding Business Context

• Requires knowledge of: Industry and environmental factors ; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices; Directly relevant business metrics and business areas
• To evaluate proposed business cases for projects and initiatives
• Translate business requirements into strategies, initiatives, and projects and aligns them to business strategy and objectives, and drives the execution of deliverables
• Builds and articulates the business case and return on investment and delivers work that has demonstrable value
• Challenge business assumptions on topics related to ones domain expertise
• Mentors the team members on new business insights and allied developments
• Proactively engages in the external community to build Walmarts brand and learn more about industry practices

Data Source Identification

• Requires knowledge of: Functional business domain and scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (eg SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success
• To Understand 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
• Reviews the deliverables of junior associates and provides guidance on data source and quality

Data Transformation and Integration

• Requires knowledge of: Internal and external data sources including how they are collected, where and how they are stored, and interrelationships, both within and external to the organization; Techniques like ETL batch processing, streaming ingestion, scrapers, API and crawlers; Data warehousing service for structured and semi-structured data, or to MPP databases such as Snowflake, Microsoft Azure, Presto or Google BigQuery; Pre-processing techniques such as transformation, integration, normalization, feature extraction, to identify and apply appropriate methods; Techniques such as decision trees, advanced regression techniques such as LASSO methods, random forests etc; Cloud and big data environments like EDO2 systems
• To build 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 comp Data Modeling Requires knowledge of: Cloud data strategy, data warehouse, data lake, and enterprise big data platforms; Data modeling techniques and tools (For example, Dimensional design and scalability), Entity Relationship diagrams, Erwin, etc; Query languages SQL / NoSQL; Data flows through the different systems; Tools supporting automated data loads; Artificial Intelligent - enabled metadata management tools and techniques
• To assemble 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 stand Code Development and Testing

• Requires knowledge of: Coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding
• To review 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 upd Data Governance

• Requires knowledge of: Data value chains; Data processes and practices; Regulatory and ethical requirements around data; Data modeling, storage, integration, and warehousing; Data value chains (identification, ingestion, processing, storage, analysis, and utilization); Data quality framework and metrics; Regulatory and ethical requirements around data privacy, security, storage, retention, and documentation; Business implications on data usage; Data Strategy; Enterprise regulatory and ethical policies and strategies
• To establish, modify, and document data governance strategies and initiatives
• Provides guidance to business stakeholders, peers, and junior associates on the implementation of data governance practices
• Helps business leaders understand and interpret company and regulatory policies around data
• Determines data governance processes, practices, policies, and guidelines
• Drives the successful adoption of organizational data utilization

Educates others on rol Data Strategy

• Requires knowledge of: Understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability etc; Understanding of the methods and applications that unlock the monetary value of data assets
• To understand, articulate, interpret, and apply principles of the defined strategy to complex business problems
• Leverages experiences and learnings to inform and influence data strategy that typically spans one or more functions or domains

Minimum Qualifications...