CBTS

Compensation

Full Description

Position Overview: The Senior Data & AI Solutions Architect will be a pivotal, client-facing role responsible for designing, selling, and delivering cutting-edge data and AI solutions. This individual will serve as a trusted advisor to cbts clients, guiding them through complex data transformations from strategic conceptualization to hands-on implementation and adoption. The role demands a unique blend of deep technical proficiency in cloud data platforms, strong architectural acumen, advanced analytical capabilities, and exceptional communication skills to translate intricate technical concepts into tangible business value. This is a highly billable role, requiring a consistent focus on client satisfaction and project profitability. Key Responsibilities: 1. Architecture & Pre-Sales: Assist in technical pre-sales engagements, collaborating with sales teams to understand client business requirements and translate them into compelling, technically sound data and AI solution architectures. Develop and present comprehensive solution proposals, technical designs, and compelling business cases that articulate the quantifiable value and Return on Investment (ROI) of proposed data and AI initiatives. Conduct technical deep-dives and workshops with prospective clients, addressing complex data integration, quality, governance, and security concerns to de-risk proposals and build client confidence. Utilize rapid prototyping and proof-of-concept development to demonstrate the tangible impact of proposed solutions, accelerating sales cycles and securing new engagements. 2. End-to-End Data Solution Delivery: Design, develop, and implement robust, scalable, and secure end-to-end data pipelines and architectures on leading cloud platforms (AWS, Azure) leveraging Snowflake and Databricks. Lead the development, validation, and operationalization of advanced analytical models and machine learning solutions, ensuring they address core business problems and deliver measurable insights. Drive the hands-on implementation, customization, and integration of data and AI solutions directly within client environments, ensuring seamless adoption and value realization. Oversee data quality, integrity, and governance throughout the solution lifecycle, establishing best practices and monitoring mechanisms. Apply DevOps/MLOps principles to automate deployment, monitoring, and maintenance of data and AI systems in production. 3. Client Relationship & Advisory: Act as a primary technical point of contact and trusted advisor for clients, fostering strong, long-term relationships built on trust and demonstrated value. Translate complex technical concepts and data insights into clear, actionable business recommendations for diverse audiences, including executive leadership and non-technical stakeholders. Collaborate closely with client teams, understanding their evolving business objectives and providing strategic guidance on the application of emerging data and AI technologies. Ensure high billable utilization rates by meticulously tracking time, managing project scope, and proactively identifying opportunities for upsell and expansion within existing client accounts. 4. Technical Leadership & Mentorship: Provide technical leadership, guidance, and mentorship to junior team members, fostering a culture of continuous learning and excellence. Contribute to cbts's internal knowledge base, best practices, and reusable assets for data and AI solution delivery. Stay abreast of industry trends, new technologies, and evolving best practices in data engineering, data science, cloud computing, and AI. Table 3: cbts Senior Data & AI Solutions Architect - Key Responsibilities & Skills Summary Category Key Responsibilities Essential Skills & Technologies Client Engagement & Sales Lead pre-sales, articulate value, conduct technical deep-dives, rapid prototyping, build client trust. Business Acumen, Value Articulation, Proposal Development, Presentation Skills, Stakeholder Management, Requirements Elicitation. Solution Architecture & Design Design scalable data/AI architectures, ensure technical feasibility, security, and cost-effectiveness. Data Modeling, Solution Design Patterns, Cloud Architecture (AWS, Azure), Data Warehousing (Snowflake), Big Data Processing (Databricks). End-to-End Delivery Implement data pipelines, develop/operationalize ML models, integrate solutions, ensure data quality, drive adoption. Python, SQL, PySpark, ETL/ELT, ML/DL Frameworks (TensorFlow, PyTorch), DevOps/MLOps, Data Governance. Core Technologies Deep hands-on experience with Snowflake, Databricks, AWS services (S3, Glue, Redshift, SageMaker), Azure services (Data Factory, Synapse, Azure ML). Specific services proficiency as detailed in Table 2. Soft Skills Exceptional communication (technical & non-technical), problem-solving, analytical thinking, client empathy, adaptability, project management. Communication, Problem-Solving, Analytical Thinking, Client Empathy, Adaptability, Project Management. Required Skills & Qualifications: Bachelor's degree in Computer Science, Data Engineering, Applied Mathematics, Statistics, or a related quantitative field. Master's degree preferred. Minimum of 8+ years of progressive experience in data engineering, data science, or forward-deployed engineering roles within a professional services or consulting environment, with a proven track record of billable client engagements. Demonstrated expertise in designing, building, and optimizing data pipelines and architectures using Python, SQL, and PySpark. Extensive hands-on experience with Snowflake and Databricks, including large-scale data migrations and complex data processing. Deep proficiency with at least one major cloud platform (AWS or Azure), including relevant data and AI services (e.g., AWS S3, Glue, Redshift, SageMaker; Azure Data Factory, Synapse Analytics, Azure ML). Solid understanding and practical experience with machine learning algorithms, model development, validation, and deployment using frameworks like TensorFlow, PyTorch, or Scikit-learn. Proven ability to translate complex business problems into data and AI solutions, and to communicate technical concepts clearly to non-technical stakeholders. Experience with data visualization tools (e.g., Tableau, Power BI, Qlik) for presenting insights and monitoring solutions. Strong understanding of data governance, data quality, and data security best practices. Experience working in an Agile development environment and familiarity with DevOps/MLOps practices. Excellent written and verbal communication, presentation, and interpersonal skills. Ability to travel to client sites as needed (up to 30%). Preferred Qualifications: Relevant cloud certifications (e.g., AWS Certified Solutions Architect, Azure Data Engineer Associate). Experience with Generative AI (GenAI) technologies and Large Language Models (LLMs). Prior experience in a billable consulting role with a strong utilization track record. Due to U.S. Government requirements applicable to foreign-owned telecommunications providers, non-US citizens may be required to submit to an extensive government agency background check which will necessitate disclosure of sensitive Personally Identifiable Information.