Engage with clients, delivery & market – Engage with new and existing clients to provide advisory consulting and address unmet needs in cloud data technologies. Proactively track D&A trends and influence client’s data strategy and choices through thought leadership. Identify white spaces and mine opportunities for growth.
Architect solutions & coordinate their implementation – Architect and design modern, high-performance D&A solutions incorporating people, process, and technology levers in cloud and big data technologies. Coordinate with delivery for adoption with focus on greater reuse, AI infusion for accelerated and risk-mitigated implementation.

Lead & support opportunities - Support pre-sales activities including conducting discovery workshops, developing point of views/solutions/scoping, writing proposals, and supporting sales and marketing enablement for Cloud and Big Data related technologies.

Guide research & experiments to address unmet needs - Proactively drive research and experimentation through the CoEs to ensure that Client stays ahead of the curve in emerging areas of cloud data engineering and architecture. Identify specific opportunities for innovation to create differentiation and address unmet needs.

Build & reuse knowledge & expertise – Build foundation components in the form of reusable assets in Cloud and Big Data technologies which can be leveraged across projects and client accounts. Advance client solutions through effective use of one or more foundation components. Influence D&A space by creating thought leadership content and participating in external events and forums. Anchor and leverage Community of Practitioners (CoPs) for knowledge sharing and mobilizing support for innovation at scale.
Nurture & grow talent – Develop differentiated D&A talent pool in through recruitment and learning & development in cloud data engineering competencies. Mentor associates to help them advance their careers in D&A using the client Career Architecture Framework.
Work and Technical Experience:

Must Have:

Experience in solution design, architecture and implementation of modern hybrid or multi-cloud data analytics solutions
Proficiency in D&A stack of Databricks on one or more hyperscaler platforms such as Azure/AWS.
Well versed with modern data pipelines on the latest services in Databricks like declarative pipelines, medallion architecture in Lakehouse
Conversant with agentic workflows using Agent bricks, context engineering, conversational interface and front end through Databricks apps as well as AI/BI Genie.
Demonstrable thought leadership through blogs, white papers and presentations in D&A events
Demonstrable presales and solutioning experience in conducting workshops, building proposals
In-depth and hands on experience on data modeling. data organization and data engineering on cloud.
Diverse D&A experiences including green field implementations, data modernization, D&A migrations from on -premise to cloud and development of full stack data products
Experience of designing and implementing domain driven architecture, data mesh and data fabric architectural patterns

Good to have:

Well versed with data management practices such as data quality management, data cataloging and data governance in hybrid and multi-cloud environments
Well versed in DataOps automation using custom or cloud native services on hyperscaler platforms
Well versed with DevSecOps considerations for cloud first data analytics solutions
Sound understanding of cloud infrastructure provision, data observability and Cloud FinOps
Demonstrable advisory and consulting experiences for strategic D&A initiatives
Sound business appreciation to understand business and functional requirements for solution architecture considerations
Experience in evaluating and comparing multiple cloud data technology choices and making right-fit recommendations in the client context
Working knowledge of frameworks like TOGAF, DAMA

Qualifications:

Bachelor’s degree in computer science, engineering, or related field (Master’s degree is a plus)
Demonstrated continued learning through one or more technical certifications or related methods
At least 14+ years of experience in data analytics with focus on cloud data technologies