Data Engineering Principal Engineer

As a data engineering lead, you will be the key technical expert overseeing PepsiCo's data product build and operations and drive a strong vision for how data engineering can proactively create a positive impact on the business. You'll be empowered to create and lead a strong team of data engineers who build data pipelines into various source systems, rest data on the PepsiCo Data Lake, and enable exploration and access for analytics, visualization, machine learning, and product development efforts across the company. As a member of the data engineering team, you will help lead the development of very large and complex data applications into public cloud environments directly impacting the design, architecture, and implementation of PepsiCo's flagship data products around topics like revenue management, supply chain, manufacturing, and logistics. You will work closely with process owners, product owners, and business users. You'll be working in a hybrid environment with in-house, on-premises data sources as well as cloud and remote systems.

Responsibilities:

• Manage a team of data engineers and data analysts by delegating project responsibilities and managing their flow of work as well as empowering them to realize their full potential.
• Design, structure, and store data into unified data models and link them together to make the data reusable for downstream products.
• Manage and scale data pipelines from internal and external data sources to support new product launches and drive data quality across data products.
• Create reusable AI-enabled accelerators and solutions to automate L3 incident resolution and triage for business-critical applications to drive productivity gains.
• Enable and accelerate standards-based development prioritizing reuse of code, adopt test-driven development, unit testing, and test automation with end-to-end observability of data.
• Build and own the automation and monitoring frameworks that capture metrics and operational KPIs for data pipeline quality, performance, and cost.
• Collaborate with internal clients (product teams, sector leads, data science teams) and external partners (SI partners/data providers) to drive solutioning and clarify solution requirements.
• Evolve the architectural capabilities and maturity of the data platform by engaging with enterprise architects to build and support the right domain architecture for each application following well-architected design standards.
• Define and manage SLAs for data products and processes running in production.

Qualifications:

• 10+ years of overall technology experience that includes at least 5+ years of hands-on software development, data engineering, and systems architecture.
• 8+ years of experience with Data Lakehouse, Data Warehousing, and Data Analytics tools.
• 6+ years of experience in data modelling using specialized modelling tools such as CA Erwin.
• 4+ years of experience in Python, PySpark, or Scala programming on big data platforms like Databricks.
• 4+ years of cloud data engineering experience in Azure or AWS.
• Fluent with Azure cloud services. Azure Data Engineering certification is a plus.
• Experience with at least one business intelligence tool such as Power BI or Tableau.
• Domain knowledge of the CPG industry with supply chain/GTM background is preferred.

Skills

AWS, Azure, Data Engineering, Databricks