Data Architect

Job Description
Object Technology Solutions, Inc (OTSI) has an immediate opening for a Data Architect.

Data Architect (Irving, TX )

Key Responsibilities Include

Understand customers’ overall data estate, IT and business priorities and success measures to design implementation architectures and solutions.
Maintain expert-level knowledge and skills to guide customer’s journey through Analytics-led Cloud Data transformation.
Apply technical knowledge to architect solutions that meet business and IT needs, create and modernize Data Platform, Data Analytics and AI roadmaps, and ensure long term technical viability of new deployments, infusing key analytics and AI technologies where appropriate (e.g. Cloud ML, ML Server, BOT framework, Cognitive Services, Big Data, Data Lake, Data Warehouse, etc.).
Ensure that solution exhibits high levels of performance, security, scalability, maintainability, appropriate reusability and reliability upon deployment.
Develop deep relationships with key customer stakeholders and IT decision makers, who drive long-term cloud adoption within their company to enable them to be cloud advocates.
Be a Voice of Customer to share insights and best practices, connect with Engineering team to remove key blockers.

Qualifications

Experiences Required - Education, Key Experiences, Knowledge and Skills:

Professional

Experience. 5 years of success in consultative/complex technical sales and deployment projects, architecture, design, implementation, and/or support of highly distributed applications required
Financial and/or international experience highly desired
Relationship Building. Proven track record of building deep technical relationships with senior IT executives in large or highly strategic accounts. Experience in managing various stakeholder relationships to get consensus on solution/projects
Problem Solving. Ability to solve customer problems through cloud technologies
Collaboration and Communication. Acknowledged for driving decisions collaboratively, resolving conflicts and ensuring follow through with exceptional verbal and written communication skills. Ability to orchestrate, lead, and influence virtual teams, ensuring successful implementation of customer projects. Presentation skills with a high degree of comfort with both large and small audiences (Senior Executives, IT management, Database administrators and Data Scientist)

Technical

Enterprise-scale technical experience with cloud and hybrid infrastructures, architecture designs, database migrations, and technology management
Breadth of technical experience and knowledge, with depth / Subject Matter Expertise in two or more of the following Data Platform Cloud solutions required:

SQL including OSS (PostgreSQL, MySQL etc), Azure SQL
NoSQL Databases including OSS (Maria, Mongo etc), Cosmos DB
Big Data and Data Warehouses including Azure Synapse Analytics (formerly Azure SQL DW), Snowflake, GCP’s Big Query, AWS Redshift
Advanced Analytics including Azure Synapse Analytics, Azure Data Bricks, visualization tools as PowerBI, Tableau
Data Governance
Data Science
Machine Learning including Azure ML, Google AI,  ML Server, Python
Artificial Intelligence including BOT framework, Cognitive Services
Expertise in two or more data estate workloads like Teradata, Oracle Exadata, IBM Netezza, SAP (HANA, BW), HDInsight, Hadoop, Cloudera/Hortonworks, Apache Spark, Snowflake, MapR, AWS (Redshift, Glue), Google (BigQuery), MemSQL, MarkLogic, Qubole, Pivotal (Greenplum), Actian (Avalanche), Alibaba (MaxCompute) required
The technical aptitude and experience to learn new technologies and understand relevant cloud trend
Competitive Landscape: Knowledge of cloud development platforms
Partners: Understanding of partner ecosystems and the ability to leverage partner solutions to solve customer needs

Education

Bachelor's degree in Computer Science, Information Technology, Engineering, or related field
Certification in one or more of the following technologies preferred: Cloud, mobile, Database, Data Engineering, Data Analytics, Big Data, BI, Data Science, Machine Learning, Artificial Intelligence.