Manager, Data Engineering

Walmart•Denver, CO

2d•$167,336 - $242,000•Onsite

About The Position

This role involves managing a team of six employees, including a Senior Data Scientist, two Data Analysts, and three Data Engineers. The position requires a strong understanding of data engineering principles, including data pipeline development, data warehousing, and data transformation. The manager will be responsible for translating business problems into data-driven solutions, identifying appropriate tools and methods, and providing recommendations to business stakeholders. The role also involves understanding business context, identifying data sources, performing data quality checks, and developing data pipelines. Expertise in various data technologies, cloud platforms, and programming languages is essential. The manager will play a crucial role in shaping the future of retail by improving data-driven decision-making.

Requirements

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.

Nice To Haves

Knowledge of Analytics/big data analytics / automation techniques and methods.
Knowledge of Business understanding.
Knowledge of Precedence and use cases.
Knowledge of Business requirements and insights.
Knowledge of Industry and environmental factors.
Knowledge of Common business vernacular.
Knowledge of 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.
Knowledge of Directly relevant business metrics and business areas.
Knowledge of Functional business domain and scenarios.
Knowledge of Categories of data and where it is held.
Knowledge of Business data requirements.
Knowledge of Database technologies and distributed datastores (e.g. SQL, NoSQL).
Knowledge of Data Quality.
Knowledge of Existing business systems and processes, including the key drivers and measures of success.
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.
Knowledge of Techniques like ETL batch processing, streaming ingestion, scrapers, API and crawlers.
Knowledge of Data warehousing service for structured and semi-structured data, or to MPP databases such as Snowflake, Microsoft Azure, Presto or Google BigQuery.
Knowledge of Pre-processing techniques such as transformation, integration, normalization, feature extraction, to identify and apply appropriate methods.
Knowledge of Techniques such as decision trees, advanced regression techniques such as LASSO methods, random forests etc.
Knowledge of Cloud and big data environments like EDO2 systems.

Responsibilities

Translate business problems into data-related or mathematical solutions.
Identify appropriate methods and tools to solve business problems.
Share use cases and examples to demonstrate how methods solve business problems.
Provide recommendations to business stakeholders to solve complex business issues.
Develop business cases for projects with projected return on investment or cost savings.
Translate business requirements into projects, activities, and tasks aligned with overall business strategy.
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.
Support the implementation of business solutions by building relationships and partnerships with key stakeholders.
Identify business needs, determine and carry out necessary processes and practices, and monitor progress and results.
Recognize and capitalize on improvement opportunities and adapt 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.
Supervise six employees: Senior Data Scientist (1), Data Analyst II (2), Data Engineer (3).