Staff Data Engineer

Walmart•Bentonville, AR

5d•Onsite

About The Position

What you'll do... Position: Staff Data Engineer Job Location: 805 Moberly Lane, Bentonville, AR 72716 Duties: Tech. Problem Formulation: Translates/co-owns business problems within one's discipline to data related or mathematical solutions. Identifies appropriate methods/tools to be leveraged to provide a solution for the problem. Shares use cases and gives examples to demonstrate how the method would solve the business problem. Understanding Business Context: 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 and develops domain specific artifact. Serves as an interpreter and conduit to connect business needs with tangible solutions and results. Identifies and recommends relevant business insights pertaining to their area of work. 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. Reviews the deliverables of junior associates and provides guidance on data source and quality. 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 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 environmental 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 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.

Requirements

Bachelor's degree or the equivalent in Computer Science or related field plus 4 years of experience in software engineering or related experience; OR Master’s degree or the equivalent in Computer Science or related field plus 2 years of experience in software engineering or related experience.
Must have experience with: Designing and implementing highly scalable, optimizing, and distributing big data pipelines and ETLs using frameworks like Hadoop, Hive, Spark, and Databricks
Developing ETL pipelines using Scala, Python, Spark, and SQL
SQL and Spark SQL querying for data analysis and validation
Spark shell scripting for automated data validation
Documenting data pipelines and workflows like Confluence, Jira, and Azure DevOps
Orchestrating and automating implemented pipeline solutions using tools and frameworks like Automic, AutoSys, Oozie, and Apache Airflow
Writing SQLs for data querying from RDBMS and Data warehousing frameworks like Apache Hive and Snowflake
Developing and maintaining modules in cloud ecosystem in Tech like Google Cloud, AWS, and Azure
Working with Parquet, AVRO, CSV, ORC, and JSON
Working in different file-based storage systems, including HDFS, GCS, Azure Blob Storage, and S3
Creating and automating dashboards by using Power BI, Tableau, and Looker
Leading large-scale projects and providing guidance to team members
System architecture, HLD to detailed design, and disaster recovery
Resource modeling, optimization, and forecasting
Applying incident management, change management, defect-reduction, and system-reliability processes to improve operational performance.
Employer will accept any amount of experience with the required skills.

Responsibilities

Tech. Problem Formulation: Translates/co-owns business problems within one's discipline to data related or mathematical solutions.
Identifies appropriate methods/tools to be leveraged to provide a solution for the problem.
Shares use cases and gives examples to demonstrate how the method would solve the business problem.
Understanding Business Context: 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 and develops domain specific artifact.
Serves as an interpreter and conduit to connect business needs with tangible solutions and results.
Identifies and recommends relevant business insights pertaining to their area of work.
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.
Reviews the deliverables of junior associates and provides guidance on data source and quality.
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 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 environmental 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 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.