DF Architecture Analyst

• Maintain a predictable, transparent, global operating rhythm that ensures always-on access to high-quality data for stakeholders across the company
• Work cross-functionally across the enterprise to centralize data and standardize it for use by business, data science or other stakeholders

Data architecture is a framework that describes how data is managed, from collection through to transformation, distribution, and consumption. It sets the blueprint for data and the way it flows through data storage systems. It is foundational to data processing operations.

Data architecture is driven by business requirements, which data architects and data engineers use to define the respective data model and underlying data structures that support it. These designs typically facilitate a business need, such as a reporting or analytics requirement. As new data sources emerge through emerging technologies, such as the Internet of Things (IoT), a good data architecture ensures that data is manageable and useful, supporting AI workloads, avoiding redundant data storage, improving data quality through cleansing and deduplication, and enabling new applications.

• Develop a deep understanding of the business domain and enterprise technology inventory to craft a solution roadmap that achieves business objectives, maximizes reuse.
• Design scalable patterns and architecture to support both batch and real-time data products & platform
• Ensure physical and logical data models are designed with an extensible philosophy to support future, unknown use cases with minimal rework.
• Partner with IT, data engineering and other teams on the administration and monitoring of all data platforms to ensure the enterprise data model incorporates key dimensions needed for the proper management: business and financial policies, security, local-market regulatory rules, consumer privacy by design principles (PII management) and all linked across fundamental identity foundations.
• Drive collaborative reviews of design, code, data, security features implementation performed by data engineers to drive data product development.
• Assist with data planning, sourcing, collection, profiling, and transformation.
• Write requirements for ETL and BI developers.
• Test the effectiveness of the database before release for business use.
• Show expertise for data at all levels: low-latency, relational, and unstructured data stores; analytical and data lakes; data streaming (consumption/production), data in-transit.
• Develop repeatable data patterns based on cloud-centric, code-first approaches to data management and cleansing.
• Work with product managers and data stewards within the enterprise data governance process to define and conceptualize data models across enterprise master data, transaction data, and informational data and implement those models into the enterprise data model.

• The expected compensation range for this position is between $75,000 - $127,500 based on a full-time schedule.
• Location, confirmed job-related skills and experience will be considered in setting actual starting salary.
• Bonus based on performance and eligibility; target payout is 5% of annual salary paid out annually.
• Paid time off subject to eligibility, including paid parental leave, vacation, sick, and bereavement.
• In addition to salary, PepsiCo offers a comprehensive benefits package to support our employees and their families, subject to elections and eligibility: Medical, Dental, Vision, Disability, Health and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan.

• 1+ years of overall technology experience that includes at least 1+ years of hands-on software development, data engineering, and systems architecture.
• 1+ years of experience with Data Lake Infrastructure, Data Warehousing, and Data Analytics tools.
• 1+ years of experience developing enterprise data models.
• 1+ years in cloud data engineering experience in at least one cloud (Azure, AWS, GCP).
• Experience in at least one data modeling tool (ER/Studio, Erwin).
• Experience with integration of multi cloud services with on-premises technologies.
• Experience with data modeling, data warehousing, and building high-volume ETL/ELT pipelines.
• Experience with data profiling and data quality tools like Apache Griffin, Deequ, and 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 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 Github and deployment & CI tools.
• Experience with Azure Data Factory, Databricks and Azure Machine learning 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.
• Working knowledge of agile development, including DevOps and DataOps concepts.
• Familiarity with business intelligence tools (such as PowerBI).
• BA/BS in Computer Science, Math, Physics, or other technical fields.

Skills, Abilities, Knowledge:

• Excellent communication skills, both verbal and written, along with the ability to influence and demonstrate confidence in communications with senior level management.
• Proven track record of leading, mentoring, hiring and scaling data teams.
• Strong change manager. Comfortable with change, especially that which arises through company growth.
• Ability to understand and translate business requirements into data and technical requirements.
• High degree of organization and ability to manage multiple, competing projects and priorities simultaneously.
• Positive and flexible attitude to enable adjusting to different needs in an ever-changing environment.
• Strong leadership, organizational and interpersonal skills; comfortable managing trade-offs.
• Foster a team culture of accountability, communication, and self-management.
• Proactively drives impact and engagement while bringing others along.
• Consistently attain/exceed individual and team goals
• Ability to lead others without direct authority in a matrixed environment
• Highly influential and having the ability to educate challenging stakeholders on the role of data and its purpose in the business.
• Understands both the engineering and business side of the Data Products released.
• Places the user in the center of decision making.
• Teams up and collaborates for speed, agility, and innovation.
• Experience with and embraces agile methodologies.
• Strong negotiation and decision-making skill.
• Experience managing and working with globally distributed teams.