Semantic Data Model Engineer - Senior

Job description

EY – Consulting - Semantic Data Model Engineer and Analyst - Senior
Top 5 prerequisite skillsexperience for candidates to train up for this role, either:

A – software engineering background

·  Software engineering / computer science degree or professional qualification
·  Practically rather than theoretically minded – ready to get hands on.  E.g. keen coder
·  Experience working with data in relational databases.  Reasonable transact SQL knowledge.
·  Experience working with XML and/or JSON files
·  Experience wrangling, transforming and analyzing datasets
·  Advantageous if candidates have experience in these areas
·  Experience in the finance/accountancy/tax domains
·  RESTful API consumption or design
·  Database schema design
·  UML modelling

B: finance (incl. bookkeeping), accountancy, tax (incl. payroll) background

·  Entry level academic or professional qualification in finance, accountancy or tax-related discipline
·  Experience analysing and classifying data – incl. very large and complex spreadsheets
·  Practically rather than theoretically minded – ready to get hands on with complex IT things.  Keen to learn to code and get under the hood.
·  Advantageous if candidates have experience in these areas
·  Technology design or build experience.  Software, services or APIs.  
·  Business process modelling, flow diagrams, etc.
·  Designing data collection templates, forms or questionnaires 

Job Summary: Semantic Data Model Analyst
EY is a global leader, driver and implementation partner of digitization programs.  This is driven by a seismic change in the digital landscape:  for example; Tax administrations are embracing APIs and establishing connected data ecosystems with industry and government, Capital Markets regulators are mandating electronic reporting, Auditors are increasingly making use of Artificial Intelligence, Machine Learning and data analytics.  At the core of these changes is the effective use, management and sharing/reporting of data assets.  The development and ongoing maintenance of data models is critical to the operation of EY’s services, our clients’ businesses and the wider functions of industries and their market supervisors and governmental bodies.
As a Semantic Data Model Engineer and Analyst you will become a member of EY’s Global Data Office (GDO).  Within the Intelligent Data Pillar (ID) you will report to the Data Standards and Models Leader (DSML) and the Data Management Lead (DML).
You will support the day-to-day delivery of development, maintenance and extension of data models and data quality capabilities managed by the Global Data Office within EY’s Trusted Data Fabric (TDF).  Working closely with other Intelligent Data pillars Global Data Office including: Organization Intelligence (OrgIntel), Data Pipelines and Data Delivery.
This hands-on role will support the team developing our data modelling and data quality services and underlying standards, principles and guidelines.  You will support peers defining, enhancing or extending reference data taxonomies and business ontologies, physical data formats, definitions and structures to support business processes and service propositions. Covering semantic and logical presentations, data quality controls, as well as physical presentations for data transport, archive and analysis needs.
This role will involve deep business analysis, as well as technical facilitation and presentation skills.
Essential Functions of the Job:  

·  Work collaboratively as a member of project teams defining, enhancing or extending data formats, reference data taxonomies and business ontologies, physical data definitions, schemas and structures for tabular/relational data as well as object-oriented data models. Including aligned physical and analytical data models for common industry models, definitions, processes and standards.  
·  Support research and evaluation of external/alliance data standards/models.
·  Focused on business analysis, interview and hold workshops with stakeholders to capture and document business-level semantic descriptions of business entities, business processes, logical relationships between facts and entities.   Ensure that reference data taxonomies and business ontologies faithfully represent the semantic meaning intended by business stakeholders and that the models are understood need business entrants.
·  Help capture data model comparability and interoperability requirements, to enable for example, the enrichment, reconciliation or comparison of client data with external/alliance data assets.
·  Support data model lifecycle management and maintenance requirements. Including the development of enhancements to reference data taxonomies and data models in response to actual usage patterns and stakeholders needs. 
·  Ensure standards, policies and processes regarding data management are followed.
·  Work with visual notations for expressing data logical and physical models, state transitions, transformation and versioning, data flows and business processes. Including UML, DPMN, ERDs, etc.
·  Working knowledge of database design patterns, SQL, XML and JSON standards, data and data type transformations. 

Knowledge and Skills Requirements:

·  Experience working with databases and data sets (Ontology, Taxonomy and Knowledge Graphs)
·  Business analysis and intelligence experience
·  Working knowledge of database design patterns, Incl. use of T-SQL
·  Ideally experience working with XML and JSON data.  Incl. data and data type transformations. 
·  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
·  Desirable, but not a prerequisite:
·  Technical facilitating and training experience advantageous
·  Familiarity or experience with visual notations for expressing data logical and physical models, state transitions, transformation and versioning advantageous. 
·  Familiarity or experience with UML and software design methodologies advantageous. 
·  Familiarity of ERP systems, external reporting tooling, data templates and standards desirable
·  Familiarity of industry standard data models and open data standards desirable
·  Familiarity with cloud and data management trends desirable
·  Experience working with data warehouse systems and enterprise data flow management systems for Hadoop, like FacebookApache Hive (incl. HiveQL) and Apache NiFi/NSA’s Niagara Files desirable

Team Responsibilities:

·  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”.  

Other Requirements:

·  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

Education:

·  A wide variety of degrees will be considered but a typical candidate may have a Bachelor’s degree in a relevant domain such as Management Information Systems, Computer Science, Information Technology, Informatics or related technical field, 
·  However, work experience will be of equal, if not greater, importance

Experience:

·  Overall 6 to 12 years of experience with a minimum of 1 year’s relevant work experience in professional data-oriented roles, including: 
·  Hands-on experience of data analysis, reporting or data transformation projects
·  Experience processing external data sources and structured 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

Certification Requirements:

·  Any data analysis and data management Vendor or Industry certification is preferred but not mandatory