Manager, Data Engineering

Walmart•Denver, CO

1d•Onsite

About The Position

This notice is being provided as a result of the filing of an Application for Permanent Alien Labor Certification. Any person may provide documentary evidence bearing on the application to the Certifying Officer of the Department of Labor: U.S. Department of Labor, Employment and Training Administration, Office of Foreign Labor Certification, 200 Constitution Avenue, NW, Room N-5311, Washington, DC 20210 Position: Manager, Data Engineering Job Location: 55 Madison Street, Suite 800, Denver, CO 80206 Duties: 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 translate/ co-own 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 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 provide 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. Identify and recommend relevant business insights pertaining to their area of work. Provides and supports the implementation of business solutions by building relationships and partnerships with key stakeholders; identifying business needs; determining and carrying out necessary processes and practices; monitoring progress and results; recognizing and capitalizing on improvement opportunities; and adapting to competing demands, organizational changes, and new responsibilities. 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 (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. To support the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for purpose. Performs initial data quality checks on extracted data. 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 extract data from identified databases. Creates data pipelines and transform data to a structure that is relevant to the problem by selecting appropriate techniques. Develops knowledge of current data science and analytics trends. This position supervises six employees: Senior Data Scientist (1), Data Analyst II (2), Data Engineer (3).

Requirements

Analytics/big data analytics / automation techniques and methods
Business understanding
Precedence and use cases
Business requirements and insights
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
Functional business domain and scenarios
Categories of data and where it is held
Business data requirements
Database technologies and distributed datastores (e.g. SQL, NoSQL)
Data Quality
Existing business systems and processes, including the key drivers and measures of success
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
Experience in the design and development of Extract, Transform, Load (ETL) pipelines to move data from source systems to data warehouses and lakes.
Experience with database technologies such as Cassandra, MongoDB, MySQL, PostgreSQL, Redis and cloud data warehouses such as Snowflake, Google Big Query and Redshift.
Experience in programming languages like Python (PySpark) for data manipulation and pipeline automation at scale.
Experience in creating interactive dashboards and reports using BI tools like Tableau, Power BI and ThoughtSpot.
Experience with statistical analysis and tools like R and Python (Pandas and NumPy).
Experience with SQL skills for querying large datasets and ensuring data quality.
Experience with best practices in data privacy and governance.
Experience with cloud platforms such as AWS, Microsoft Azure and Google Cloud Platform for big data storage, computer services, and data pipeline orchestration.
Experience with various revenue sources including impression data from ad tech and revenue systems.
Experience developing, maintaining, and optimizing data pipelines using modern tech stack (Databricks, Snowflake, and Apache Airflow).
Expertise in television, soundbar and user data collected from various sources (Internet of Things (IoT), software, and external sources).
Master's degree or the equivalent in Computer Science, Engineering, Data Science or related field and 1 year of experience in software engineering, data engineering, database engineering, business intelligence, business analytics or related field; OR Bachelor’s degree or the equivalent in Computer Science, Engineering, Data Science or related field and 3 years of experience in software engineering, data engineering, database engineering, business intelligence, business analytics or related field.

Responsibilities

Translate/ co-own business problems within one's discipline to data related or mathematical solutions.
Identify appropriate methods/tools to be leveraged to provide a solution for the problem.
Share use cases and give examples to demonstrate how the method would solve the business problem.
Provide recommendations to business stakeholders to solve complex business issues.
Develop business cases for projects with a projected return on investment or cost savings.
Translate business requirements into projects, activities, and tasks and align to overall business strategy and develop domain specific artifact.
Serve as an interpreter and conduit to connect business needs with tangible solutions and results.
Identify and recommend relevant business insights pertaining to their area of work.
Provide and support the implementation of business solutions by building relationships and partnerships with key stakeholders; identifying business needs; determining and carrying out necessary processes and practices; monitoring progress and results; recognizing and capitalizing on improvement opportunities; and adapting to competing demands, organizational changes, and new responsibilities.
Support the understanding of the priority order of requirements and service level agreements.
Help identify the most suitable source for data that is fit for purpose.
Perform initial data quality checks on extracted data.
Extract data from identified databases.
Create data pipelines and transform data to a structure that is relevant to the problem by selecting appropriate techniques.
Develop knowledge of current data science and analytics trends.