Senior Data Architect
4 weeks ago
- Design, architect, andimplement a scalable data platform for the businessunit's specific needs that supports data-drivendecision-making, operations efficiency, and democratization ofdata
- Design a data platform architecture thatpromotes seamless integrations with 3rd party applications,Corporate IT systems including ERP, and in-house builtsolutions
- Act as the primary liaison betweenthe business unit technology team and Corporate IT, ensuring thatdata architecture, governance standards, cyber security andcompliance protocols are maintained
- Establishprocess to ensure federation of corporate IT policies within thebusiness unit
- Lead and mentor a team of dataengineers to build all necessary data pipelines and infrastructuresinspired by latest best practices in modular design andmicroservices.
- Collaborate with businessstakeholders, product managers, software engineers, and other dataengineers to ensure the data platform supports the innovationambitions of the business unit including Data Science, MachineLearning, and Artificial Intelligence
- Developand implement logical and physical data models that meet the uniqueneeds of the business unit while aligning with Corporate ITstandards for data architecture.
- Develop andoversee data ETL processes guaranteeing optimizedperformance
- Maintain detailed documentation ofthe data architecture, data flows, and integrations to ensuretransparency and consistency across both business unit andcorporate data systems.
Qualifications:
- Bachelor's degree inComputer Science, Computer Engineering, Data Science or a relatedfield
- A Master in the field is aplus
- 8 years experience in data architecture,data engineering, with significant experience in enterprise datadesign.
1.Data ArchitectureDesign
- ArchitectingData Solutions: Design and implement scalable andhigh-performance data architectures that meet both current andfuture business needs.
- DataModeling: Develop data models (conceptual, logical,and physical) to structure and organize data in a way thatmaximizes its value while ensuring data integrity andefficiency.
- Big DataSolutions: Design and implement data architecturesto handle large volumes of data, including the use ofNoSQL databases (e.g.,Cassandra,MongoDB) and distributedsystems (e.g., Hadoop,Spark).
- CloudArchitecture: Create and manage cloud-based dataarchitectures using platforms likeAWS, GoogleCloud, orAzure.
- DataWarehousing: Lead the design and implementation ofdata warehouses, data lakes, and ETL (Extract, Transform, Load)pipelines to consolidate data from multiplesources.
- DataIntegration: Design and implement systems for dataintegration, ensuring data flows seamlessly between systems and isharmonized for analysis.
2.Data Governance andSecurity
- DataGovernance: Establish policies and frameworks formanaging data quality, consistency, and availability across theorganization.
- DataSecurity: Ensure that data is securely stored andprocessed, adhering to privacy regulations (e.g.,GDPR,CCPA) and implementing encryption,access control, and auditingmeasures.
- Compliance:Ensure that data architecture complies with industry standards,security practices, and regulatoryrequirements.
- MetadataManagement: Implement and manage metadata systems totrack and govern the usage, lineage, and transformation ofdata.
3. CollaborationandLeadership
- Cross-DepartmentCollaboration: Work closely with teams such as dataengineering, business intelligence, data science, and productmanagement to define data needs and ensure alignment across theorganization.
- Mentoring:Provide leadership and mentorship to junior data architects, dataengineers, and other technical teams to ensure the adoption of bestpractices.
- StakeholderCommunication: Present and explain technicalconcepts to non-technical stakeholders, including senior managementand business leaders, helping them understand the strategic valueof data architecture.
- ProjectManagement: Lead or participate in the planning andexecution of data-related projects, ensuring they are delivered ontime and meet business goals.
4.Optimization and PerformanceTuning
- DataProcessing Optimization: Continuously assess andimprove the performance of data systems, including optimizingqueries, data pipelines, and storagesolutions.
- Scalability:Ensure that data solutions are scalable to handle growing datavolumes and changing businessneeds.
- DisasterRecovery: Design and implement disaster recoverystrategies to ensure that data is protected and can be quicklyrestored in case of a failure.
5.Innovation andStrategy
- EvaluateNew Technologies: Stay up-to-date with the latesttrends and technologies in data architecture, including AI/ML, datalakes, real-time analytics, and cloudplatforms.
- Long-TermStrategy: Define and implement the long-term datastrategy, ensuring that the architecture aligns with the evolvingneeds of the business and technologicaladvancements.
- EmergingTrends: Explore and incorporate new technologies,tools, and best practices to improve data architecture, such asdata mesh, datafabric, and serverlessarchitectures.
Data Processing,DataCollection,Data Analysis and Reporting
Employment Type : Full-time
Department / Functional Area: DataEngineering
Experience: NotMentioned years
Gender: Male
Vacancy: 1
Joining Date: Thu, 27 Feb 2025
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