Principal Data Engineer, Manager

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

PepsiCo is using the power of data to transform the way our world famous portfolio of brands are sold every day. Our Data Science & Analytics group influences every aspect of how we sell and move our products. In just a short period of time, they’ve built new capabilities that have defined the data science roadmap across all of our brands. Members of our team solve complex problems facing our rapidly changing business and get to see their work come to life in the real world.

Join PepsiCo’s Data Management and Operations team which develops quality data collection processes, maintains the integrity of our data foundations and enables business leaders and data scientists across the company to have rapid access to the data they need for decision-making and innovation.

Role summary

As a Lead Data Engineer, you will be the key technical expert overseeing PepsiCo's data product build & 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 & 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.

What will you be doing more specifically

• Provide leadership and management to a team of data engineers, managing processes and their flow of work, vetting their designs, and mentoring them to realize their full potential.
• Manage a data engineering project module: responsible for clarifying initial objectives, setting up methodology, and quality assurance in line with overall project goals
• Act as a subject matter expert across different digital projects.
• 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.
• Build and own the automation and monitoring frameworks that captures metrics and operational KPIs for data pipeline quality and performance.
• Responsible for implementing best practices around systems integration, security, performance and data management.
• Empower the business by creating value through the increased adoption of data, data science and business intelligence landscape.
• Collaborate with internal clients (data science and product teams) to drive solutioning and POC discussions.
• Evolve the architectural capabilities and maturity of the data platform by engaging with enterprise architects and strategic internal and external partners.
• Develop and optimize procedures to “industrialize” data science models.
• Define and manage SLAs for data products and processes running in production.

Qualifications/Requirements

What you will bring:

• 6+ years of experience with software development, data engineering, or systems architecture:
• 6+ years of experience with Data Lake Infrastructure, Data Warehousing, and ETL/ELT pipelines.
• 6+ years of experience in SQL optimization and performance tuning, and development experience in programming languages like Python, PySpark, Scala etc.).
• 4+ years in cloud data engineering experience in at least one cloud (Azure, AWS, GCP).
• Proven track record of leading, mentoring, hiring and scaling data teams.

Experience in the following areas:

• Multi cloud services integration with on-premises technologies.
• Data profiling and data quality using tools like Apache Griffin, Deequ, and Great Expectations.
• MPP database technologies such as Redshift,SnowFlake, preferable Synapse.
• Systems of data extraction, ingestion, and processing of large data sets.
• Azure Data Factory, Azure Databricks and Azure Machine learning tools
• Version control systems like Github and deployment & CI tools.
• Cloud infrastructure applications and containerized services like Kubernetes.
• Working knowledge of agile development, including DevOps and DataOps concepts.
• Excellent communication skills, both verbal and written, along with the strong ability to influence senior level management, stakeholders, and data science team.
• Strong change manager: comfortable with change, especially that which arises through company growth.
• Communicate with business domain experts, data analysts and engineering teams to build foundational, well-understood data sets
• High degree of organization and ability to manage multiple, competing projects and priorities simultaneously.

What makes us different?

• Hybrid work model
• Entrepreneurial environment in leading international company
• Professional growth possibilities & learning opportunities
• Diverse team from over 25 countries
• Variety of benefits to support your physical, emotional, and financial wellbeing
• Free Friday afternoon & during the month of August
• Relocation package & sponsorship to work in Spain if applicable