Senior Manager, Data Engineering - Catalog

Position Summary... What you’ll do...

Tech Problem Formulation Requires knowledge of Analytics big data analytics automation techniques and methods Business understanding Precedence and use cases Business requirements and insights To translate coown business problems within ones discipline to data related or mathematical solutions Identifies appropriate method stools to be leveraged to provide a solution for the problem Shares use cases and gives examples to demonstrate how the method would solve the business problem Data Governance Requires knowledge of Data value chains Data processes and practices Regulatory and ethical requirements around data Data modeling storage integration and warehousing Data value chains identification ingestion processing storage analysis and utilization Data quality framework and metrics Regulatory and ethical requirements around data privacy security storage retention and documentation Business implications on data usage Data Strategy Enterprise regulatory and ethical policies and strategies To establish modify and document data governance projects and recommendations Implements data governance practices in partnership with business stakeholders and peers Interprets company and regulatory policies on data Educates others on data governance processes practices policies and guidelines Provides recommendations on needed updates or inputs into data governance policies practices or guidelines Data Source Identification Requires knowledge of Functional business domain and scenarios Categories of data and where it is held Business data requirements Database technologies and distributed data stores eg SQL No

SQL Data Quality Existing business systems and processes including the key drivers and measures of success To Understand the priority order of requirements and service level agreements Defines and identifies the most suitable sources for required data that is fit for purpose referring to external sources as required Performs initial data quality checks on the extracted data Reviews the deliverables of junior associates and provides guidance on data source and quality Data Transformation and Integration Requires knowledge of Internal and external data sources including how they are collected where and how they are stored and interrelationships both within and external to the organization Techniques like ETL batch processing streaming ingestion scrapers API and crawlers Data warehousing service for structured and semi-structured data or to MPP databases such as Snowflake Microsoft Azure Presto or Google Big Query Preprocessing techniques such as transformation integration normalization feature extraction to identify and apply appropriate methods Techniques such as decision trees advanced regression techniques such as LASSO methods random forests etc Cloud and big data environments like EDO2 systems To build the infrastructure required for optimal transformation and integration from a wide variety of data sources using appropriate data integration technologies Uses modern tools techniques and architectures to partially or completely automate the most common repeatable and tedious data preparation and integration tasks Deploys pipelines using scheduling and orchestration frameworks Evaluates impacts of data issues and risks at an early stage Identifies needs and creates methods to fuse and reshape complex multisource data and make it usable for modeling Updates knowledge of current and emerging big data analytics and data science trends and techniques Data Modeling Requires knowledge of Cloud data strategy data warehouse data lake and enterprise big data platforms Data modeling techniques and tools For example Dimensional design and scalability Entity Relationship diagrams Erwin etc Query languages SQL No

SQL Data flows through the different systems Tools supporting automated data loads Artificial Intelligent enabled metadata management tools and techniques To assemble large complex data across all data platforms for example relational dimensional No

SQL and data tools Builds complex logical and conceptual models and provides guidance to team on physical data models…