Data Scientist - Big4

- At EY we believe that it is important for the Data Office to be a multi-disciplinary. This requires that you demonstrate your ability to operate within a diverse team and expand your area of influence beyond "traditional data management".

To satisfy these objectives, your team responsibilities will include: - Contribute to the Data Ecosystem team to execute the charter of the GDO

- Engage with internal stakeholders where there will be influence but not authority

- Work independently under minimal guidance with regular alignment with DSML and DML

- Skills and attributes for success

Knowledge and Skills Requirements:

- Create and maintain optimal data pipeline architecture,

- Experience in enterprise data assets in a large, complex organization(s), Assemble large, complex data sets that meet functional / non-functional business requirements with an understanding of industry standard data models and open data standards

- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.

- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and Leading Cloud solutions and 'big data' technologies.

- Experience working with data warehouse systems and enterprise data flow management systems for Hadoop, Hive, Spark etc.

- Working knowledge of database design patterns, SQL, XML and JSON standards, data and data type transformations. Hands on experience in ETL tools like:- Alteryx, SSIS, Informatica

- 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 Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.

- Keep our data separated and secure across national boundaries through multiple data centers and Leading Cloud solutions.

- Experience in Azure cloud solutions is an added advantage

- Exposure to Python related Big data solution, like - PySpark, Pandas, NumPy

- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.

- Work with data and analytics experts to strive for greater functionality in our data systems.

- Experience in understanding, managing and developing data models and stores

- Familiarity of ERP systems, external reporting tooling, data templates and standards are an added advantage for the role Business analysis and intelligence experience, with familiarity of modern, commercial business processes.

- Experience in supply chain commercial data exchange, internal and external reporting requirements

(operational, management, financial, tax and supervisory), financial auditing processes, etc.will be an added advantage. Working use of visual notations for expressing data logical and physical models, state transitions, transformation and versioning. Ideally also data flows and business processes. Including UML, DPMN, ERDs, etc.

- Ability to analyze complex situations and to derive workable actions

- Ability to constructively challenge requirements and current state to increase overall value to the firm

- Strong relationship building skills

- Ability to understand and integrate cultural differences and motives and to lead virtual cross-cultural, cross-border teams

- Flexibility to adjust to multiple demands, shifting priorities, ambiguity and rapid change

- Excellent organization, written and verbal communication

- Knowledge of b2b commercial supply chain - Technical facilitating and training experience

- To qualify for the role you must have

- A minimum of 3-5 years relevant work experience in professional data-oriented roles, including: - Hands-on experience of data modelling projects, including the maintenance of legacy production data models

- Experience processing external data sources and structed data formats

- Demonstrated experience in business analysis and intelligence roles (with clear understanding of the use and application of data within business processes)

- Experience of having been engaged within a multi-cultural, multi-disciplined, globally dispersed team See above for knowledge and skill requirements Ideally, you'll also have Moderate travel

- Long hours may occasionally be required to meet project commitments and/or preparing materials for clients (internal and external). Overtime may be required as per country overtime policy

- Flexibility in working hours to accommodate workload and multiple time zones, as needed

What we look for - Manage the day-to-day delivery of development, maintenance and extension of data managed by the Global Data Office within EY's Trusted Data Store.

- Coordinate, technically train and lead project teams defining, enhancing or extending data formats, taxonomies, schemas and structures.

- In conjunction with the Market Activation Lead (MAL), the Data Standards and Models Leader (DSML), the Data Management Lead (DML), the Data Quality Leader (DQL), Data Governance Lead (DGL) and the Data Discovery Leader (DDL) to identify and prioritize data modelling work.

- Work with Data Pipeline Lead (DPL) to capture data model comparability and interoperability requirements, to enable for example, the enrichment, reconciliation or comparison of client data with external/alliance data assets.

- With the Data Standards and Models Leader (DSML), deliver aligned physical and analytical data models for common industry models, definitions, processes and standards. Support research and evaluation of external/alliance data standards/models.

- Led by the Data Governance Lead (DGL), support the application of data policies and processes and their continued enforcement to include areas such as workflow for data ingestion, data quality, master data and data life cycle management.

- To ensure that all data assets which are subject to the agreed governance processes within the Data office.

- Data model lifecycle management. Monitor usage of the data models and data model extensions within EY. Develop enhancements in response to actual usage patterns and stakeholders needs. Including depreciation of data model assets.

- Ensure standards, policies and processes regarding data management are followed, Work with Data Discovery Leader to ensure data assets can be clearly catalogued (identifiable and discoverable) in the Trusted Fabric.