Pune, Maharashtra
Job Summary
Data Modeller cum Architect - Databricks / AWS /RestfulAPI
About Data Platforms
Data Platforms is a technology group that develop and support a large suite of applications, functions and services with the emphasis on data and interfaces which underpin the Barclays Private Bank and Wealth business. We provide a core Data Warehouse platform but also a number of business and integration type services to allow different areas of the bank to leverage the data. Over the next few years, our core platform and services will migrate to AWS where we aim to modernise the data estate.
Overall purpose of role
The successful candidate will be a key player in the design and growth of the data integration platform which supports our global Wealth businesses. The platform integrates data from a range of sources and makes them available to consuming systems servicing day-to-day operations, reporting to regulators, and management information and business intelligence stakeholders. The platform is undergoing expansion and growth through 2021 and beyond, with many new data sources to be onboarded; therefore, we require a senior data warehouse and data integration consultant who would work closely with our Data Architect and with analysis and development teams to:
assist stakeholders with their data requirements
gather and manage in-depth knowledge of our data platform’s data models
create and maintain data models and metadata
design (but not build) changes and extensions to the platform’s data models and data flows
perform impact assessment for required changes and
define requirements and lead problem solving in the data architecture space bringing together people of different business and technical disciplines.
Key Responsibilities
Key Accountabilities
Identify and assess potential data sources
Carry out Impact Assessment and create user stories
Define business transformation rules from source system through to consumer interface
Build and maintain knowledge of data sources and what data is used by a particular Consumer
Definition of data models and metadata in line with business requirements and metadata standards
Validate data model is in line with projected data growth, non-functional requirements and changing consumer patterns
Educate users on the platform’s capabilities, the data available, what it means and how to access it
Stakeholder Management and Leadership
Stakeholder management with all parties involved in a project ranging from programme, architecture, application SMEs, control tribes.
Provide leadership to drive delivery and across a project team.
Decision-making and Problem Solving
Decision making in proposing technical direction, solving problems and recommending appropriate strategies. Candidate will be required to determine when to facilitate resolution and when to take ownership / drive resolution.
Risk and Control Objective
Ensure that all activities and duties are carried out in full compliance with regulatory requirements, Enterprise Wide Risk Management Framework and internal Barclays Policies and Policy Standards.
Person Specification
Self-driven, proactive and demonstrates initiative with strong problem solving abilities
Strongly collaborative in nature with the ability to see the whole picture
Highly communicative and influential, able to manage conflict with ability to express technical complexities in accessible terms
Confident and assertive in nature
Skill Requirements
Essential Skills/Basic Qualifications:
Data modelling (definition of data schemata) and data analysis skills in a data warehouse type environment, including both star schema and third normal form. Using ER Studio or similar tool
Strong SQL skills. Comfortable at the SQL command prompt
Experience of working in Financial domain with understanding of key data entities in Investments and Client domain
Understand types of data schema and when to use them (3NF, star, flat, etc); mapping/translation between these
Express key structural points of complex data model in accessible terms
Experience understanding source data models with limited documentation and access to experts
Understanding of the principles of data quality and data governance
Desirable skills/Preferred Qualifications:
Strong analytical skills
Experience of inferring a data model and lineage from an existing database with limited documentation
Python and Pandas for data manipulation
Data warehouse background
Experience of data science and machine learning principles and techniques
ETL tools; ability to use and enhance new tooling
Amazon Web Services (AWS) – AWS Glue, Athena, S3
Data catalogue e.g. Alation
Working in agile mode
Graduate preferably with a technology based degree
Other Requirements
1. Databricks Certified Data Engineer Professional Or Databricks Certified Data Architect (Optional But Valuable).
2. Microsoft Azure Solutions Architect Expert Or Aws Certified Solutions Architect (Optional But Valuable)
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