Senior Business Analytics Engineer

Company Description
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands. At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.

Position Overview
Based in Bengaluru, India this Data Engineer will work as part of an elite team alongside data engineers and data visualization engineers focused on maximizing value from data while working on high priority business opportunities across all functions and geographies. The Data Engineer will collaborate with line of business users, business analysts, data analysts and data scientists on models and algorithms to deliver analytics insights and use cases. The Data Engineer will leverage analytical, visualization, and data engineering skills using data to solve problems, unlock opportunities and create new insights. They will identify, explore, acquire, and transform internal and external data sets. They will use storytelling with data to share insights and inspire data driven actions. This role will require both creative and collaborative working with IT and the wider business. It will involve evangelizing effective data management practices and promoting better understanding of data and analytics. The Data Engineer will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics and data science solutions.

Essential Duties & Responsibilities
Build quick data pipelines: The primary responsibility of data engineers is to build quick data pipelines that will provision data ready for analysis to be utilized for Data Science and Machine Learning. This includes internal enterprise data assets and external second- and third-party data. The engineer should be a creative problem solver able to identify new opportunities that can be rapidly prototyped and evaluated working closely with stakeholders. Analyze data to unlock insights: Move beyond descriptive reporting and data engineering by helping stakeholders identify relevant insights and actions from data. Use ML techniques, regression, cluster analysis, time series, etc. to explore relationships and trends in response to stakeholder questions and business challenges. Create visualizations and tell great stories with data: The Data Engineer must be able to communicate insights in a way that invites understanding and compels action across multiple levels of the organization. Educate and train: The engineer should be curious and knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new requirements. They will also be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration and operationalization techniques in optimally addressing these data requirements. The engineer will be required to train counterparts such as data scientists, data analysts, LOB users or any data consumers in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases. Become a data and analytics evangelist: The engineer will be considered a blend of data and analytics "evangelist," "data guru" and "fixer." This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.

Experience, Skills & Knowledge

* A bachelor's or master's degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field is required.
* The ideal candidate will have a combination of analytical skills, data governance skills, IT skills and Retail industry knowledge with a technical or computer science degree.
* At least 5 years or more of work experience in analytical or business intelligence disciplines including data analysis, visualization, integration, modeling, etc.
* At least 3 years of experience working in cross-functional teams and collaborating with business stakeholders in Retail in support of a departmental and/or multi-departmental analytics initiative.
* Deep Retail Industry knowledge or previous experience working in the business would be a plus. Technical Knowledge/Skills
* Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as Python, R, Scala, or similar.
* Experience in working with atleast one or two cloud services from AWS, Azure or Google cloud platforms and experience in working with Spark using Python or Scala.
* Any cloud certification would be a plus
* Good understanding of analytical methods including regression, forecasting, time series, cluster analysis, classification, etc. Experience with machine learning and AI would be a plus.
* Strong experience with popular database programming languages including SQL, PL/SQL, etc. for relational databases and knowledge on NoSQL/Hadoop oriented databases like MongoDB, Cassandra, etc for nonrelational databases.
* Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. These should include ETL/ELT, data replication/CDC, message-oriented data movement and upcoming data ingestion and integration technologies such as stream data integration and data virtualization.
* Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production.
* Experience working with popular data discovery, analytics and BI software tools like MicroStrategy, Tableau, Qlik, PowerBI and others for semantic-layer-based data discovery. Certification in one more of these tools would be a plus.
* Basic understanding of popular open-source and commercial data science platforms such as Python, R, KNIME, Alteryx, others is a strong plus.
* Basic experience in working with data governance, data quality, and data security teams and specifically and privacy and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification.
* Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service and others.
* Adept in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization
* Interpersonal Skills and Characteristics
* Strong experience supporting and working with cross-functional teams in a dynamic business environment.
* Required to be highly creative and collaborative. An ideal candidate would be expected to collaborate with both the business and IT teams to define the business problem, refine the requirements, and design and develop data deliverables accordingly. The successful candidate will also be required to have regular discussions with data consumers on optimally refining the data pipelines developed in nonproduction environments and deploying them in production.
* Required to have the accessibility and ability to interface with, and gain the respect of, stakeholders at all levels and roles within the company.
* Is a confident, energetic self-starter, with strong interpersonal skills.
* Has good judgment, a sense of urgency and has demonstrated commitment to high standards of ethics, regulatory compliance, customer service and business integrity.