84.51° Overview:

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

Join us at 84.51°!

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LEAD DATA ENGINEER – P3330

84.51° Overview:

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

Join us at 84.51°!

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As Lead Data Engineer, your role will involve collaboration with cross-functional teams, leveraging cutting-edge technologies, and ensuring scalable, efficient, and secure data engineering practices. You’ll be working with data across 2 cloud platforms (Azure and GCP) and thus will be working with a wide variety of technologies. A strong emphasis will be placed on expertise in GCP, Vertex AI, and advanced feature engineering techniques.

Responsibilities
Take ownership of systems, processes, and the tech stack while driving features to completion through all phases of the entire 84.51° SDLC. This includes internal and external facing applications as well as process improvement activities:

Provide Technical Leadership: Offer technical leadership to ensure clarity between ongoing projects and facilitate collaboration across teams to solve complex data engineering challenges.
Build and Maintain Data Pipelines: Design, build, and maintain scalable, efficient, and reliable data pipelines to support data ingestion, transformation, and integration across diverse sources and destinations, using tools such as Kafka, Databricks, and similar toolsets.
Drive Digital Innovation: Leverage innovative technologies and approaches to modernize and extend core data assets, including SQL-based, NoSQL-based, cloud-based, and real-time streaming data platforms.
Implement Feature Engineering: Develop and manage feature engineering pipelines for machine learning workflows, utilizing tools like Vertex AI, BigQuery ML, and custom Python libraries.
Implement Automated Testing: Design and implement automated unit, integration, and performance testing frameworks to ensure data quality, reliability, and compliance with organizational standards.
Optimize Data Workflows: Optimize data workflows for performance, cost efficiency, and scalability across large datasets and complex environments.
Mentor Team Members: Mentor team members in data principles, patterns, processes, and practices to promote best practices and improve team capabilities.
Draft and Review Documentation: Draft and review architectural diagrams, interface specifications, and other design documents to ensure clear communication of data solutions and technical requirements.
Cost/Benefit Analysis: Present opportunities with cost/benefit analysis to leadership, guiding sound architectural decisions for scalable and efficient data solutions.

Requirements:
Bachelor’s degree typically in Computer Science, Management Information Systems, Mathematics, Business Analytics or another STEM degree.

Proven experience in leading cross-functional teams and managing complex projects from inception to completion
5+ years of professional Data Development experience.
5+ years of experience with SQL and NoSQL technologies.
3+ years of experience building and maintaining data pipelines and workflows.
2+ years of experience developing with Python.
3+ years of experience developing Kafka solutions.
2+ years of experience in feature engineering for machine learning pipelines.
Experience with GCP services such as BigQuery, Vertex AI Platform, Cloud Storage, AutoMLOps, and Dataflow.
Experience with CI/CD pipelines and processes.
Experience with automated unit, integration, and performance testing.
Experience with version control software such as Git.
Full understanding of ETL and Data Warehousing concepts.
Strong understanding of Agile principles (Scrum).

Preferred Qualifications

Knowledge of Structured Streaming (Spark, Kafka, EventHub, or similar technologies).
Experience with GitHub SaaS/GitHub Actions.
Experience understanding Databricks concepts.
Experience with PySpark and Spark development.
Experience with Service Oriented Architecture.

IMPORTANT INFO

Candidates must be able to come into the office on Monday, Tuesday, Wednesday and Thursday of each week.

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