Data Management & Operations Associate Manager
About the position
Design, build, and maintain backend systems and services by applying full-stack software development principles using Python, ensuring scalable and maintainable architecture across various applications. Develop robust APIs and microservices using frameworks like Flask, FastAPI, and Ariadne, while integrating with data processing libraries such as Pandas and ORMs like SQLAlchemy to support business logic and data workflows. Architect and manage data lake infrastructure and data warehouse solutions, enabling efficient ingestion, transformation, and storage of large datasets for downstream analytics and reporting needs. Design, implement, and optimize relational and non-relational databases, including Oracle, PostgreSQL, Azure SQL, and MongoDB, ensuring high performance, security, and availability of data assets. Create and maintain high-volume ETL/ELT pipelines to process structured and semi-structured data, supporting analytics use cases and enabling near real-time insights from diverse data sources. Automate deployment workflows using DevOps practices, including containerizing applications with Docker, configuring Helm charts, and deploying to Azure Kubernetes Service (AKS) for scalable production environments. Manage source code and infrastructure automation using Git, GitHub Actions, and Azure DevOps, enabling continuous integration and delivery pipelines across multi-cloud and hybrid environments. Develop data-driven solutions by analyzing large datasets, building predictive models, and leveraging LLMs to extract insights, automate text analysis, and enhance business decision-making through advanced generative AI techniques.
Telecommuting permitted 30%: work may be performed within normal commuting distance from the PepsiCo office in Plano, TX.
Responsibilities
• Design, build, and maintain backend systems and services using Python.
• Develop APIs and microservices using frameworks like Flask, FastAPI, and Ariadne.
• Architect and manage data lake infrastructure and data warehouse solutions.
• Design, implement, and optimize relational and non-relational databases.
• Create and maintain high-volume ETL/ELT pipelines.
• Automate deployment workflows using DevOps practices.
• Manage source code and infrastructure automation using Git, GitHub Actions, and Azure DevOps.
• Develop data-driven solutions by analyzing large datasets, building predictive models, and leveraging LLMs.
Requirements
• Bachelor's (US or Foreign Equivalent) in Computer Science, Information Technology, Data Science, or related and six (6) years of experience in Software development, Data Science and Data Analytics.
• Must have five (5) years of experience in: Hands-on experience on software development and system architecture using Python with extensive knowledge in at least three of the following components: Flask, FastAPI, Ariadne, Pandas, or SQL alchemy; Cloud data engineering experience in at least one cloud (Azure, AWS, GCP); Version control systems such as Github and deployment & CI tools; Working knowledge of agile development, including DevOps and DataOps concepts; System/Cloud security tools SAML or OKTA; and Database management, designing, optimizing, and managing SQL and NoSQL databases, such as Oracle, PostgreSQL, Azure SQL, and MS SQL.
• Must have four (4) years of experience in: Building high-volume ETL/ELT pipelines. Data profiling and data quality tools; Familiarity with business intelligence tools (Power-Bi or Tableau). Building custom reporting tools with UI technologies in at least three of the following: jQuery, JavaScript, React JS, Angular JS, or FASTAPI;
• Must have two (2) years of experience in Collecting and pre-processing data, performing statistical analysis, building ML/NLP models, engineering features, and delivering insights through predictive modeling, text analytics, and performance monitoring.
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Design, build, and maintain backend systems and services by applying full-stack software development principles using Python, ensuring scalable and maintainable architecture across various applications. Develop robust APIs and microservices using frameworks like Flask, FastAPI, and Ariadne, while integrating with data processing libraries such as Pandas and ORMs like SQLAlchemy to support business logic and data workflows. Architect and manage data lake infrastructure and data warehouse solutions, enabling efficient ingestion, transformation, and storage of large datasets for downstream analytics and reporting needs. Design, implement, and optimize relational and non-relational databases, including Oracle, PostgreSQL, Azure SQL, and MongoDB, ensuring high performance, security, and availability of data assets. Create and maintain high-volume ETL/ELT pipelines to process structured and semi-structured data, supporting analytics use cases and enabling near real-time insights from diverse data sources. Automate deployment workflows using DevOps practices, including containerizing applications with Docker, configuring Helm charts, and deploying to Azure Kubernetes Service (AKS) for scalable production environments. Manage source code and infrastructure automation using Git, GitHub Actions, and Azure DevOps, enabling continuous integration and delivery pipelines across multi-cloud and hybrid environments. Develop data-driven solutions by analyzing large datasets, building predictive models, and leveraging LLMs to extract insights, automate text analysis, and enhance business decision-making through advanced generative AI techniques.
Telecommuting permitted 30%: work may be performed within normal commuting distance from the PepsiCo office in Plano, TX.
Responsibilities
• Design, build, and maintain backend systems and services using Python.
• Develop APIs and microservices using frameworks like Flask, FastAPI, and Ariadne.
• Architect and manage data lake infrastructure and data warehouse solutions.
• Design, implement, and optimize relational and non-relational databases.
• Create and maintain high-volume ETL/ELT pipelines.
• Automate deployment workflows using DevOps practices.
• Manage source code and infrastructure automation using Git, GitHub Actions, and Azure DevOps.
• Develop data-driven solutions by analyzing large datasets, building predictive models, and leveraging LLMs.
Requirements
• Bachelor's (US or Foreign Equivalent) in Computer Science, Information Technology, Data Science, or related and six (6) years of experience in Software development, Data Science and Data Analytics.
• Must have five (5) years of experience in: Hands-on experience on software development and system architecture using Python with extensive knowledge in at least three of the following components: Flask, FastAPI, Ariadne, Pandas, or SQL alchemy; Cloud data engineering experience in at least one cloud (Azure, AWS, GCP); Version control systems such as Github and deployment & CI tools; Working knowledge of agile development, including DevOps and DataOps concepts; System/Cloud security tools SAML or OKTA; and Database management, designing, optimizing, and managing SQL and NoSQL databases, such as Oracle, PostgreSQL, Azure SQL, and MS SQL.
• Must have four (4) years of experience in: Building high-volume ETL/ELT pipelines. Data profiling and data quality tools; Familiarity with business intelligence tools (Power-Bi or Tableau). Building custom reporting tools with UI technologies in at least three of the following: jQuery, JavaScript, React JS, Angular JS, or FASTAPI;
• Must have two (2) years of experience in Collecting and pre-processing data, performing statistical analysis, building ML/NLP models, engineering features, and delivering insights through predictive modeling, text analytics, and performance monitoring.
Apply tot his job
Apply To this Job