Essential Functions

• Drives world-class design and development of analytical data pipelines.
• Provides thought leadership and propose industry standards and implementations.
• Adhere to processes to ensure data pulled from various sources meets quality standards, is curated and enhanced for analytical use and there is a "single source of truth"
• Work with counterparts from Tech to build frameworks that integrate data pipelines and machine learning models that facilitate use by data scientists for priority use cases;
• Enterprise Data and Analytics team focused on "last mile" transformations on select data required for use cases
• Maintain database structure and standard definitions for business users across Macy's
• Work with data architects to build the foundational extract / load / transform process and regularly review the architecture and recommend effectiveness improvements
• Collaborate with Technology to future-proof data & analytics software, tools, and code to reduce risk and support pipeline owners
• Work with Legal and Privacy teams to adhere to data privacy and security standards
• Work with Data Architect to implement the data models, standards, and quality rules
• Work with the Data Science team to understand data formatting and sourcing needs to enable them to build out use cases as efficiently as possible.
• Keep data separated and secured using masking and encryption.
• Explain the requirement to offshore team and create Interface to determine the most efficient and cost-effective approach to meet business requirements. 
• Daily onsite-offshore coordination

 

Qualifications and Competencies

• At least 8 years of overall experience in building ETL/ELT, data warehousing and big data solutions.
• At least 5 years of experience in building data models and data pipelines to process different types of large datasets.
• At least 3 years of experience with Python, Spark, Hive, Hadoop, Kinesis, Kafka.
• Proven expertise in relational and dimensional data modeling.
• Understand PII standards, processes, and security protocols.
• Experience in building data warehouse using Cloud Technologies such as AWS or GCP Services, and Cloud Data Warehouse preferably Google BigQuery.
• In depth knowledge in Big Data solutions and Hadoop ecosystem.
• Strong SQL knowledge – able to translate complex scenarios into queries.
• Strong Programming experience in Java or Python
• Experience in Google Cloud platform (especially BigQuery & Dataflow)
• Experience with Google Cloud SDK & API Scripting
• Experience in Hadoop ( Hive, MapReduce, Spark )
• Experience in Onsite-Offshore coordination
• Experience in Test Driven Development
• Experience in Agile processes and DevOps methodologies.
• Experience in NoSQL Databases
• Experience with Data modeling and mapping. 
• Experience of Retail domain will be an added advantage
• Google Cloud Professional Data Engineer Certification is an advantage.
• Programming Language – Java / Python
• Google Cloud – Big Query, Pub/ Sub, Dataflow, Composer DAGs, Cloud storage
• CI/CD – GitHub, Jenkins