The Kraft Heinz Company is revolutionizing the food industry – we will be the most profitable food company powered by the most talented people with unwavering commitment to our communities, leading brands and highest product quality in every category in which we compete. As a global food and beverage powerhouse, Kraft Heinz represents over $29 billion in revenue and is the 3rd largest food and beverage company in North America and 5th largest in the world. At Kraft Heinz, to be the BEST food company, growing a BETTER world is more than a dream – it is our GLOBAL VISION. To be the best, we want the best – best brands, best practices and, most importantly, the best people.
We are looking for a Manager for Data Engineer to join our growing team of analytics experts. The hire will be responsible for introducing technical capabilities that will expand and optimize our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is the perfect blend of data engineer and business data analyst who enjoys optimizing data systems, building them from the ground up, and educating others on the value of the data and how it can support various business use cases. The Manager Data Engineer will manage our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of joining a team whose goal is to create the most valuable enterprise data assets and passionate about the problems these assets can help solve.
This is a role for someone who craves a challenge balanced by the desire to be accountable for delivering in the largest business unit for Kraft Heinz. This role demands constant interaction with internal colleagues and external partners from different functions and backgrounds.
Manage data ingestion projects within the Snowflake/Azure architecture.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
Work with stakeholders including the IT Service Delivery and Global Business Services organizations to assist with data-related technical issues and support their data needs.
Work with data and analytics experts to strive for greater functionality in our Hadoop data systems.
3+ years of rapid career advancement in a data analytics delivery role
Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Strong analytic skills related to working with unstructured datasets.
Build processes supporting data transformation, data structures, metadata, dependency and workload management.
A successful history of manipulating, processing and extracting value from large disconnected datasets.
Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
3+ years of experience in a Data Engineer role. They should also have experience using the following software/tools:
Big data tools: Snowflake, Hadoop, Spark, Kafka, etc.
Relational SQL and NoSQL databases, including Postgres and Cassandra.
Data pipeline and workflow management tools: Azure DevOps, Azkaban, Luigi, Airflow, etc.
Stream-processing systems: Storm, Spark-Streaming, etc.
Object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
Text processing scripting languages: Sed and AWK
REST API Pipelines for data extraction
DevOps tools: Azure DevOps, , Jenkins, Git, Maven, SBT, etc.