Architect Lead, Data Platform Architecture

Position Summary:

Architect Lead, Data Architecture is a senior leadership role in Enterprise Architecture Organization. This role will have a broad array of resposnibilities across Data Architecture, Data Platform and leads enterprise wide initiatives  

Essential Duties and Responsibilities:

• Develop data architecture and strategy to support successful business outcomes.
• Define wholsitic data architecture for data analytics applications including data ascience and AI.
• Proactively works with product manager and Architect Lead to identify Data architectural solution blueprints needed for solution build, manage the commitment and timeline
• Drives the creation and validation of non-functional requirements for the data platform concerning items such as consistency, availability, partitioning, sharding, performance, security, resiliency etc.
• Maintains the blueprint along with working towards making vendor recommendations, providing candidate solutions and allowing the adoption of the best solution that will meet the program needs but also be aligned with the overall enterprise goals. 
• Lead review of design and assessing the feasibility of implementation and recommending options, constraints and alternatives to product team especially when complex requirements are prohibited from implementation due to existing constraints
• Liaison with other architects to understand the integration scenarios and provide solutions to the cross-program blueprint level dependency analysis 
• Responsible for reviewing the proposed data architecture for data analytics applications including data ascience and AI solution blueprints with Engineering Lead and Product Manager prior to implementation
• Leads creating the review material for presentation to architecture review board (ARB) where required and get closure on the solution path to be undertaken for implementation
• Leads PoC’s where necessary to prove solution direction in coordination with product and engineering lead around resource capacity and priority
• Providing collaborative support towards implementation efforts - with engineering team, platform team, QA team and so on, by providing Q&A and offering help to the build team where necessary
• Conducts periodic implementation review to examine whether the solution aligns with the blueprint level architecture and suggest remediation/correction actions necessary within sprint or PI
• Help examine recurring issues to identify underlying gaps in architecture or implementation and coming up with suggestive remediative list for engineering teams to review and implement 
• Collaborate in designing deployment solution in coordination with devops team
• Maintain a collective portfolio level documentation of solution blueprints that express the makeup of the program and the solution’s continuous alignment to enterprise architecture standards
• Identify opportunities for improvement in any area, whether it be people, process or technology of the program in an ongoing manner
• Contributes to establishing software principles, blueprints and standards.
• promotes best practices for effective use of software technology and services.
• Plays a leadership role in defining systems integration processes and practices.
• Advocates for process reviews in on-going IT projects or services under defined IT policies.
• Monitors organizational and functional adherence to IT policies and procedures when addressing risk management.
• Leads discussions and answers complex questions regarding cross-functional IT policies and standards.
• Contributes to the establishment and use of best practices in IT policies, standards and procedures.
• Creates criteria to measure the effectiveness of IT policies, standards and procedures.
• Forecasts technological industry trends and potential risks in the implementation of defined IT policies, standards and procedures.
• Responsible for ensuring the following backlogs are maintained in JIRA
  • Active backlog for work to be performed within Increment, with dependent engineering or infrastructure tasks where possible (short range planning)
  • Future backlog for work to be performed now to enable future Increments (long range planning)
  • Review backlog for remediation work discovered by architects in current sprints so as to further allow the engineering teams to validate and improve the quality of work delivered

Financial Responsibilities

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None

Qualifications:

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• Bachelor’s or Masters in Computer Science/Engineering or related disciplines.

Knowledge and Skills:

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• 15+ years of experience with a wide variety of data platforms both on-premises and on AWS including different types of databases such as Oracle, Db2, Cassandra, Couchbase, Realm, MongoDb
• 5+ years of experience with architecting and designing Cloud based data platform including Amazon Aurora, RDS, S3, EC2, EFS, Hadoop.
• Deep expertise with data model design including traditional RDBMS, Document NoSQL, Wide-column NoSQL, Key-Value Database, conversion of relational data models to NoSQL data models.
• Expert in understanding the Consistency, Availability, and Partitioning (CAP) capabilities of SQL and NoSQL databases and experience with tuning Consistency capabilities of Eventually consistent No SQL Databases.
• Strong experience with architecting and delivering data and Big data platforms capabilities such as database Clustering, Replication, Disaster Recovery, Ship to Shore data replication, resiliency, maintaining data integrity.
• Hands on experience with large scale package implementations like Siebel CRM, PeopleSoft.
• Deep experience with data ER modeling and mapping the ER model to SQL and No SQL models including Cassandra, Couch DB, Relational DBs.
• Extensive experience data platform high availability design both on-premises and on cloud.
• Broad experience with performance tuning on traditional RDBMS and NoSQL platforms. in a large enterprise.
• Experience with AI/ML including Supervised learning, Unsupervised learning, and reinforced learning. Supervised learning algorithms including Logistic Regression and the Back Propagation Neural Network and Unsupervised learning algorithms including Apriori algorithm and K-Means with Python
• Experience with AWS ML, or Google AI Services, or MS Azure. Databricks, Azure Data Lake is must.
• Experience with application middleware technologies family of products is a plus
• Architecture and design experience with platform implementations across Business, Application, Data, and Platform architecture.
• In depth knowledge in Data Ingestion, Data pipeline design for extracting,transforming data from heterogeneous data sources to a target state architecture data source.
• Platform design and implementation experience with projects that encompass digital, webservices, microservices, API gateways etc.
• Experience with docker/container platforms that includes Kubernetes, Mesosphere, etc.
• Experience in administering messaging solutions that include IBM MQ, Tibco EMS, Confluent Kafka, etc.
• Strong knowledge of Linux, UNIX, Windows operating systems and virtualization technologies
• Experience with middleware clustering and load balancing
• Working knowledge of Microsoft Azure, AWS, GCP
• Proficiency in project estimations, capacity planning, availability planning and performance
• Documentation and development of processes and procedures.
• Automation and Scripting experience that includes designing and building data pipelines
• Experience with Architecture frameworks like Zachman, FEA, TOGAF, MODAF/DODAF

Physical Demands:

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Work Environment:

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