Enterprise Data Operations Manager

PepsiCos Data Management and Operations team is tasked with the responsibility of developing quality data collection processes, maintaining the integrity of our data foundations, and enabling business leaders and data scientists across the company to have rapid access to the data they need for decision-making and innovation.

What PepsiCo Data Management and Operations does:

Maintain a predictable, transparent, global operating rhythm that ensures always-on access to high-quality data for stakeholders across the company.

Responsible for day-to-day data collection, transportation, maintenance/curation, and access to the PepsiCo corporate data asset

As a data engineering lead, 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. 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.

Ideally Candidate must be flexible to work an alternative schedule either on tradition work week from Monday to Friday; or Tuesday to Saturday or Sunday to Thursday depending upon coverage requirements of the job. The candidate can work with immediate supervisor to change the work schedule on rotational basis depending on the product and project requirements.

• 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 accelerators and solutions to migrate data from legacy data warehouse platforms such as Teradata to Azure Databricks
• 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 captures metrics and operational KPIs for data pipeline quality, performance and cost.
• Responsible for implementing best practices around systems integration, security, performance, and data management.
• Collaborate with internal clients (product teams, sector leads, data science) and external partners (SI partners/data providers) to drive solutioning and clarify solution requirements.
• Overseework with internal clients and external partners to
• 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.
• Support large-scale experimentation done by data scientists.
• Prototype new approaches and build solutions at scale.
• Create documentation for learnings and knowledge transfer to internal associates.

• 13+ years of overall technology experience that includes at least 5+ years of hands-on software development, data engineering, and systems architecture.
• 6+ years of experience with Data Lake Infrastructure, Data Warehousing, and Data Analytics tools.
• 6+ years of experience in SQL optimization and performance tuning on MS SQL Server, Azure SQL or any other popular RDBMS
• 6+ years of experience in Python and Pyspark/Scala programming on big data platforms like Databricks
• 4+ years in cloud data engineering experience in Azure or AWS.
• Fluent with Azure cloud services. Azure Data Engineering certification is a plus.
• Experience with integration of multi cloud services with on-premises technologies.
• Domain Knowledge in CPG industry with Supply chain/GTM background is preferred.
• Experience with data modelling, data warehousing, and building high-volume ETL/ELT pipelines.
• Experience with data profiling and data quality tools like Great Expectations.
• Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets.
• Experience with at least one MPP database technology such as Databricks, Redshift, Synapse or Snowflake.
• Experience with running and scaling applications on the cloud infrastructure and containerized services like Kubernetes.
• Experience with version control systems like ADO, Github and CI/CD tools for deployment.
• Experience with Azure Data Factory, Azure Databricks and Azure Machine learning tools.
• Experience with Statistical/ML techniques is a plus.
• Experience with building solutions in the retail or in the supply chain space is a plus.
• Understanding of metadata management, data lineage, and data glossaries is a plus.
• Familiarity with business intelligence tools (such as PowerBI).
• BA/BS in Computer Science, Math, Physics, or other technical fields.
• Candidate must be flexible to work an alternative work schedule either on tradition work week from Monday to Friday; or Tuesday to Saturday or Sunday to Thursday depending upon product and project coverage requirements of the job.
• Candidates are expected to be in the office at the assigned location at least 3 days a week and the days at work needs to be coordinated with immediate supervisor