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About the Role

Provides pre-sales technical support to field sales teams, ensuring the technical validity and interoperability of solutions and aligning them with customer strategic business plans.

Responsibilities

• Provides technical expertise to sales organization in selecting, implementing, and developing competitive product and services applications and solutions.
• Delivers technical presentations to showcase product capabilities and applications to technical users/buyers.
• Prepares detailed product specifications for the purpose of selling high-end product and solutions.
• Provides project scoping and co-ordinates internal specialists and inter-department activities.
• Assists sellers in creating demand for product.

Requirements

• Hands-on experience with at least one major cloud data platform (e.g., Snowflake, Databricks, BigQuery, Redshift, Cloudera, Synapse, or similar).
• Strong understanding of data warehousing, data lakes/lakehouse, and ETL/ELT concepts (staging, modeling, performance tuning, cost/perf tradeoffs).
• Data engineering and integration including unstructured data processing (PDFs, logs, images, text) and transformation into structured/vectorized formats.
• Strong SQL skills for analytical queries, performance tuning, and data modeling (star/snowflake schemas, dimensional modeling, partitioning, clustering).
• Understanding of vector databases (e.g., Elasticsearch, Milvus, pgvector), embedding models, and RAG architectures.
• Familiarity with document processing pipelines, chunking strategies, and semantic search patterns.
• Familiarity with data pipeline and orchestration tools (e.g., Airflow, dbt, Spark, Kafka, cloud-native ETL tools) and batch vs. streaming patterns.
• Understanding of data governance (catalog, lineage, security, RBAC, masking, compliance requirements like GDPR/CCPA).
• Ability to design and explain analytics solutions end-to-end: from raw data to dashboards and predictive models.
• Working knowledge of BI tools (e.g., Tableau, Power BI, Looker, Qlik) and how to connect, model, and optimize for self-service analytics.
• Familiarity with data science and ML workflows (feature engineering, experimentation, model training/deployment, RAG pipeline development, prompt engineering) and tools/languages such as Python, Spark, notebooks, and ML frameworks (e.g., scikit-learn, MLflow, TensorFlow/PyTorch, LangChain, LlamaIndex at a conceptual level).
• Skilled at asking the right questions to uncover technical requirements, constraints, and business drivers.
• Can translate ambiguous business problems into clear data and analytics use cases.
• Excellent at translating complex technical topics into clear, business-oriented narratives for both technical and non-technical audiences.
• Comfortable presenting to large groups and senior stakeholders (CIO/CDO, Heads of Data/Analytics).
• Able to build and deliver compelling demonstrations that tell a story around customer data and use cases, not just features.
• Can structure and run POCs with clear success criteria, timelines, and executive readouts to accelerate technical win.
• Understands the broader data & AI ecosystem and can articulate differentiation versus other data warehouses, data lake/lakehouse platforms, and analytics tools.
• Experience with cloud data warehouse or lakehouse migrations.
• Experience with enterprise BI modernization/self-service analytics.
• Experience with GenAI and RAG implementations for enterprise knowledge management, intelligent document processing, or customer-facing AI applications.
• Experience with real-time or streaming analytics.
• Experience with advanced analytics / data science enablement.
• Hands-on experience with at least one major public cloud (AWS, Azure, or GCP) and one or more leading data platforms (e.g., Snowflake, Databricks, Cloudera, BigQuery, Redshift, Synapse).
• Proven experience architecting and delivering data management, analytics, or data science solutions in one or more of the specified areas.

Skills

• Cloud data platforms
• Data warehousing
• Data lakes/lakehouse
• ETL/ELT concepts
• Data engineering
• Data integration
• Unstructured data processing
• SQL
• Vector databases
• Embedding models
• RAG architectures
• Document processing pipelines
• Chunking strategies
• Semantic search patterns
• Data pipeline and orchestration tools
• Batch vs. streaming patterns
• Data governance
• Analytics
• BI
• Data science
• Analytics solutions design
• BI tools
• Self-service analytics
• Data science and ML workflows
• Python
• Spark
• Notebooks
• ML frameworks
• LangChain
• LlamaIndex
• Technical requirements gathering
• Business problem translation
• Storytelling
• Communication
• Presentation skills
• Demonstration building
• POC structuring and execution
• Competitive positioning
• Cloud data warehouse migrations
• Enterprise BI modernization
• GenAI implementations
• Real-time analytics
• Streaming analytics
• Advanced analytics
• Public cloud (AWS, Azure, GCP)
• Leading data platforms

Experience Level

• 5+ years in a customer-facing technical role