Presales Solution Architect- Data Analytic

Solution Architect cum Presales Leader

Key Responsibilities

Presales & Client Engagement

• Partner with sales and business teams to identify opportunities in Data Engineering, Analytics, BI, and AI/ML.
• Conduct discovery workshops with clients to understand pain points, data landscape, and business objectives.
• Prepare winning RFP/RFI responses, solution decks, cost models, and PoCs.
• Deliver demos, technical presentations, and thought leadership sessions to CXO and senior stakeholders.
• Define value propositions, ROI models, and differentiators for data/analytics opportunities.

Solution Architecture & Design

• Design end-to-end Data & Analytics architectures including:
• Data Engineering: ingestion, pipelines, ETL/ELT, data lakes, warehouses, lakehouses.
• Analytics: BI dashboards, reporting, self-service analytics, advanced analytics.
• AI/ML Integration: predictive, prescriptive, NLP, GenAI, and data-driven automation.
• Define reference architectures leveraging modern platforms (Snowflake, Databricks, BigQuery, Synapse, Redshift, etc.).
• Architect scalable solutions on AWS, Azure, or GCP cloud ecosystems.
• Ensure security, compliance, and data governance are embedded in solutions.
• Build reusable assets, accelerators, and frameworks for faster presales cycles.

Delivery Enablement & Governance

• Collaborate with delivery leads to ensure solution feasibility and alignment.
• Provide technical governance during transition/mobilization phases.
• Define SLAs, KPIs, and governance frameworks for data & analytics programs.
• Support estimation of efforts, resources, and delivery planning.

Innovation & Thought Leadership

• Drive innovation in AI/ML, GenAI for data, and modern data architectures (lakehouse, data mesh).
• Mentor solution consultants and delivery architects on presales and solutioning best practices.
• Represent the organization in conferences, client forums, and leadership workshops.

Key Performance Indicators (KPIs)

• Win ratio on data/analytics opportunities supported.
• Revenue growth influenced by data/analytics deals.
• Solution accuracy vs. delivery execution (variance 10%).
• Number of PoCs, accelerators, and reusable assets created.
• Customer CSAT/NPS for delivered data programs.

Required Skills & Qualifications

Technical Skills

• Strong expertise in Data Engineering: ETL/ELT, data lakes, warehouses, real-time streaming (Kafka, Kinesis, Spark, Flink).
• Proficiency in Analytics & BI tools: Power BI, Tableau, Qlik, Looker.
• Knowledge of Data Science/AI frameworks: Python (pandas, scikit-learn), TensorFlow, PyTorch, NLP/LLMs.
• Experience with Cloud Platforms: AWS (Redshift, Glue, Athena), Azure (Synapse, Data Factory), GCP (BigQuery, Dataflow).
• Familiarity with Data Governance, MDM, and Data Security frameworks.
• Understanding of modern architectures: data mesh, data fabric, lakehouse.