Senior Data Engineering Manager/Architect

Reports to: Director, Analysis and Category BI Analytics

The Senior Manager, Data Engineering and Architecture will play an integral role in creating a new core capability for the company by overseeing the acquisition, quality assurance, and delivery of large volumes of data in support. This role is central to the goal of rapidly increasing our capability to quickly access and analyze data for insights that enable the teams to drive improvements in business-critical areas like forecast accuracy, revenue management, consumer journey, customer service, assortment optimization, etc. that will further the overall company objectives around enabling growth, cash conversion, and productivity.

In this role, you will work closely with a multidisciplinary, agile team to build high-quality data pipelines driving analytic solutions. These solutions will generate insights from our connected data, enabling the company to advance data-driven decision-making capabilities across the enterprise. You will have a deep understanding of data architecture, data engineering, and reporting, and a basic understanding of data science techniques and workflows and the business processes supported by the data pipeline.

Examples of problems you will help solve alongside the data science teams include determining optimal product assortments and next-generation products, reimagining consumer engagement with personally relevant content, and reinventing our supply chain as the company continues to integrate its several businesses into a streamlined, highly efficient unified corporation.

We are expressly seeking a candidate who can provide thought leadership in the data engineering space.

Specific Responsibilities

- Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals.

- Evolve the architectural capabilities and maturity of the data platform by engaging with enterprise architects and strategic internal and external partners.

- Oversee work with internal clients and external partners to structure and store data into unified taxonomies and link them together with standard identifiers.

- Manage and scale data pipelines from internal and external data sources to support new product launches and drive data quality across data products.

- Specify and implement best practices around systems integration, security, performance, and data management.

- Create data products for data science and business analyst team members to improve their productivity.

- Foster a culture of sharing, re-use, and design for scale, stability, and operational efficiency of data and analytical solutions.

- Lead the evaluation, implementation, and deployment of emerging tools and processes for analytic data engineering in order to improve our productivity as a team.

- Partner with data scientists, machine learning engineers, business intelligence engineers, and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.

Key Qualifications

- 10+ years of overall technology experience that includes at least 6+ years of hands-on software development, data engineering, and systems architecture.

- Academic training in a quantitative field such as Computer Science, Mathematics, Statistics, Bioscience, or Engineering. Bachelor's degree required; Master's degree or equivalent professional experience preferred.

- Experience and expert familiarity with software development and data engineering best practices, code testing, and using CI/CD tools in the workflow.

- Expert command of SQL and Python; familiarity with R a plus.

- Solid experience with developing solutions on cloud computing services (Azure, AWS, GCP) and infrastructure in the data and analytics space; previous experience with Azure specifically a plus.

- Database development experience using Hadoop, SPARK, or BigQuery and experience with a variety of relational, NoSQL, and cloud database technologies.

- Experience with data lake infrastructure, data warehousing, and data analytics tools.

- Experience with orchestration tools such as Airflow.

- Experience with containerized solutions such as Docker and ML orchestration tools such as Kubernetes or MLFlow a plus.

- Expert with computing resources running the Linux operating system

- Good written and oral communication skills and the ability to use these to convey results

comprehensibly to a non-technical audience.

- Ability to work with and connect with all levels of employees. Experience working in a matrix environment a plus.

- Good planning and organizational skills.

- Initiative and follow-up, including a strong bias for action.

- Strong character-ability to champion the right decision in difficult situations.

- Demonstrated ability to handle multiple tasks and assignments simultaneously.