Actively contribute to code development in projects and services
Manage and scale data pipelines from internal and external sources to support new product launches and ensure data quality
Build and own automation and monitoring frameworks to capture metrics and operational KPIs for data pipeline quality and performance
Implement best practices for systems integration, security, performance, and data management
Empower the business through increased adoption of data, data science, and business intelligence
Collaborate with internal clients to drive solutioning and proof of concept discussions
Develop and optimize procedures to operationalize data science models
Define and manage SLAs for data products and processes in production
Support large-scale experimentation by data scientists
Prototype new approaches and build solutions at scale
Research state-of-the-art methodologies
Create documentation for learnings and knowledge transfer
Create and audit reusable packages or libraries
Qualifications Required:
4 years of overall technology experience with at least 3 years in software development, data engineering, and systems architecture
3 years of experience in Data Lake Infrastructure, Data Warehousing, and Data Analytics tools
3 years of experience in SQL optimization, performance tuning, and programming languages such as Python, PySpark, Scala, etc. - 2 years of cloud data engineering experience in Azure, with fluency in Azure cloud services
Experience with multi-cloud services integration and on-premises technologies
Proficiency in 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
Familiarity with MPP database technologies such as Redshift, Synapse, or SnowFlake
Experience in running and scaling applications on cloud infrastructure and containerized services like Kubernetes
Proficiency in version control systems like Github and deployment & CI tools
Experience with Azure Data Factory, Azure Databricks, and Azure Machine learning tools is a plus
Familiarity with Statistical/ML techniques, retail, or supply chain solutions
Understanding of metadata management, data lineage, and data glossaries
Working knowledge of agile development, including DevOps and DataOps concepts
Proficiency in business intelligence tools like PowerBI
BA/BS in Computer Science, Math, Physics, or other technical fields
Key Responsibilities:
Actively contribute to code development in projects and services
Manage and scale data pipelines from internal and external sources to support new product launches and ensure data quality
Build and own automation and monitoring frameworks to capture metrics and operational KPIs for data pipeline quality and performance
Implement best practices for systems integration, security, performance, and data management
Empower the business through increased adoption of data, data science, and business intelligence
Collaborate with internal clients to drive solutioning and proof of concept discussions
Develop and optimize procedures to operationalize data science models
Define and manage SLAs for data products and processes in production
Support large-scale experimentation by data scientists
Prototype new approaches and build solutions at scale
Research state-of-the-art methodologies
Create documentation for learnings and knowledge transfer
Create and audit reusable packages or libraries
Qualifications Required:
4 years of overall technology experience with at least 3 years in software development, d
