Solutions Architect Ii

**Purpose of the Job**:
As a recognized data engineering authority the Solution Architect collaborates in data engineering standards & controls and creates the data engineering architecture for the target environments to deliver data pipelines into the bigger Business Intelligence environments.
The solution architect will collaborate closely with the data, integration and analytics teams.

**Job Objectives**:
Define and continuously improve the data engineering architecture framework and solution architecture modeling standards.
Define and create solution level data engineering architecture models that enable specific BI and analytics requirements.
Provide expert data engineering guidance, ensure solution architectures and designs are in line with the data engineering technology standards and conduct architecture and design reviews as part of the Architecture Review Committee.
Define a structured data engineering architecture approach and methodology for capturing the key views of the enterprise.
Architect the next-generation Big Data analytics framework developed on a group of core technologies.
Align to the enterprise data reference architecture in support of the enterprise and regulatory information governance needs such as Information Security, Enterprise Information Management,
POPI, PCI, etc.
Identify, define and communicate standards, guidelines, formats, meta-models, policies, best practices, and governance practices for Data Engineering architectures and designs.
Ensure that the Data Engineering approach integrates into the methodologies and processes of the rest of the Enterprise Architecture team.
Stay abreast of best practices and / or new developments in Data engineering and related disciplines and drive adoption as deemed appropriate.
Analyze the inputs and outputs of BI and analytics and create related Data engineering architectures and designs for the baseline (“as is”) and target (“to be”) solution architectures.
Coordinate with system analysts, development teams and DBAs to ensure creation of physical
database and an optimal implementation of the data engineering design.
Define data engineering architecture governance processes and quality compliance criteria.
Perform quality assurance checks on Data engineering Architectures and Designs and enforce quality compliance criteria to set policies and standards.
Perform quality checks on existing Data Engineering Architectures and Designs to identify potential business risks areas and make re engineering recommendations.
Ensure Data engineering security conforms to Information Security Governance policies and standards.
Define and develop the overall data engineering architecture landscape in partnership with the
Domain Architect, Data Analytics team and other Solution Architects.
Review proposed solution architectures ensuring alignment with architecture principles, the architecture framework, cloud reference architectures, set technology standards and identify critical gaps, and recommend improvements.
Give guidance and advice to peers in respect to data engineering solution designs ensuring the designs conform to industry best practices and standards

**Qualifications**:
Degree in Information Systems / B Sc. Comp. Science (or similar)

**Experience**:
Working experience within the IT industry
Leadership role in an Information and Data analysis environment
Experience with IT projects from an Information and Data architecture and design perspective (SDLC).
Knowledge of various architecture frameworks including TOGAF and
Zachman frameworks (TOGAF certification would be a distinct advantage)
Proven experience creating data engineering solutions, building and maintaining reliable and scalable pipelines on big data platforms
Experience in working with Data Ops and/or Dev Ops Frameworks
Knowledge of implementing future-fit Data Engineering technologies and capabilities such as APIs, API Portals, and Lakehouse architectures.
Proven Experience in using scripting languages such as Python and SQL to create, manage, manipulate data pipelines and - sets
Proven experience with data engineering in a cloud environment (AWS/Azure/Google) as well as on-premise.
Experience with data profiling, data cleansing/quality, performance issues, reporting and data analysis
Experience in database and data management (Data Replication, Backup and Recovery, DBMS Scaling techniques, SOA, ODS/OLTP vs Data Warehouse/OLAP/VLDB)
Experience in various data modeling paradigms (Dimensional, Columnar, Data Vault, NoSQL)
Experience in implementing and using EA tools and EA meta-model definition (ARIS Preferable/ or Sparx Enterprise Architect)

**Knowledge and Skills**:
Strong analytical, problem-solving and logical skills with attention to detail
Excellent team-working, interpersonal skills
Delivery of compelling presentations to all levels of stakeholders and excellent communication and relationship building skills
Develop of proposals and marketing materials and excellent