Overview
We are looking for a Data Analytics Senior Engineer to join our Integrated Business Planning (IBP) team, which is responsible for building scalable and efficient data solutions for demand planning, supply planning, finance monetization, and other areas. This role will be a mix of Data Engineering and Data Analysis. You will take ownership of assigned areas both technically and functionally and will be empowered to lead the assigned areas end to end.
You will work on designing, developing, and maintaining a delta data lake, optimizing data pipelines, finding synergies between different functional datasets, and transforming raw data into gold-standard datasets. This role is ideal for someone who enjoys working with complex multi-functional data and wants to have a direct impact on business strategy.
Working with inspiring and experienced colleagues, you'll find that the atmosphere in our office in Barcelona is informal and engaging. With an active, get-things-done culture, this is a place where your dynamism and agility will make a difference. As part of the IBP EDO Analytics team, you will be the key technical and functional expert developing and overseeing PepsiCo's data product build & operations and drive a strong vision for how data Analytics can proactively create a positive impact on the business. You'll be an empowered member of a team of data Analysts/Engineers who build data pipelines to Extract, Load, and Transform the data based on the functional requirements and support analytics, visualization, and product development efforts across the company. As a member of the data 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 demand planning, 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-premise data sources as well as cloud and remote systems.
Responsibilities
Your day to day with us:
1. Be an active contributor to code development in projects and services.
2. Develop functional knowledge of the assigned areas and work as SME taking end-to-end responsibility.
3. Manage and scale data pipelines from internal and external data sources to support new product launches and drive data quality across data products.
4. Build and own the automation and monitoring frameworks that capture metrics and operational KPIs for data pipeline quality and performance.
5. Responsible for implementing best practices around systems integration, security, performance, and data management.
6. Empower the business by creating value through the increased adoption of data, data science, and business intelligence landscape.
7. Collaborate with internal clients (data science and product teams) to drive solutioning and POC discussions.
8. Develop and optimize procedures to “productionalize” data science models.
9. Define and manage SLAs for data products and processes running in production.
Qualifications
1. 5+ years of overall technology experience that includes at least 2+ years of hands-on building data engineering pipelines.
2. 3+ years of experience in SQL optimization and performance tuning, and development experience in programming languages like Python, PySpark, Scala, etc.
3. 3+ years in cloud data engineering experience in Azure; Azure Certification is a plus.
4. 3+ years in building data engineering pipelines in Azure (ADF and Databricks).
5. Experience with version control systems like GitHub and deployment & CI tools.
6. Experience with Statistical/ML techniques is a plus.
7. Experience with building solutions in planning, retail, finance, or supply chain space is a plus.
8. Understanding of metadata management, data lineage, and data glossaries is a plus.
9. Working knowledge of agile development, including DevOps and DataOps concepts is a plus.
10. BA/BS in Computer Science, Math, Physics, or other technical fields.
#J-18808-Ljbffr