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Data Engineer
hace 1 día
Overview:
PepsiCo operates in an environment undergoing immense and rapid change. Big-data and digital technologies are driving business transformation that is unlocking new capabilities and business innovations in areas like eCommerce, mobile experiences and IoT. The key to winning in these areas is being able to leverage enterprise data foundations built on PepsiCo’s global business scale to enable business insights, advanced analytics and new product development. PepsiCo’s 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
- Work cross-functionally across the enterprise to centralize data and standardize it for use by business, data science or other stakeholders
- Increase awareness about available data and democratize access to it across the company
**Responsibilities**:
Key Accountabilities:
- Active contributor to code development in projects and services.
- 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.
- Develop and optimize procedures to “productionalize” data science models.
- Define and manage SLA’s for data products and processes running in production.
- Support large-scale experimentation done by data scientists.
- Prototype new approaches and build solutions at scale.
- Research in state-of-the-art methodologies.
- Create documentation for learnings and knowledge transfer.
- Create and audit reusable packages or libraries.
Qualifications:
Qualifications:
- 2+ years of overall technology experience that includes at least 2+ years of hands-on software development, data engineering, and systems architecture.
- 2+ years of experience in SQL optimization and performance tuning, and development experience in programming languages like Python, PySpark, Scala etc.).
- 1+ years in cloud data engineering experience in Azure Certification is a plus.
- 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 version control systems like Github and deployment & CI 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.
- 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 data teams.
- Strong change manager. Comfortable with change, especially that which arises through company growth. Able to lead a team effectively through times of change.
- 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.
Differentiating Com
