Pre-Sales Technical Solutions Engineer – Data Analytics & AI

Role Summary

As a Pre-Sales Technical Solutions Engineer, you will be the technical bridge between our sales team and prospective clients. You will lead discovery sessions, architect data and AI solutions, deliver compelling technical presentations and demos, and craft responses to RFPs/RFIs. This role demands deep expertise in enterprise data platforms, analytics, machine learning, and the ability to translate complex business needs into clear and credible data-driven roadmaps for organizations with 1,000+ employees. You will focus exclusively on modern data engineering, analytics, BI modernization, machine learning, and AI platform design.

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Key Responsibilities

• Partner with Account Executives to qualify analytics and AI opportunities and conduct technical discovery workshops with C-suite, VP, and Director-level stakeholders.
• Design high-level data and AI architectures (data pipelines, ingestion patterns, analytics layers, ML workflows, and governance models) tailored to enterprise requirements.
• Deliver technical demos, POCs, and whiteboard sessions showcasing data modernization, AI/ML capabilities, and platform automation.
• Lead RFP/RFI responses including technical write-ups, estimates, migration strategies, risk assessments, and TCO/ROI models.
• Develop reusable data and AI reference architectures, engineering patterns, and demo assets.
• Stay ahead of industry trends in advanced analytics, real-time data processing, MLOps, generative AI, and data governance.
• Collaborate with delivery teams to ensure proposed data and AI solutions are feasible, scalable, and profitable.

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Preferred Experience

• 7+ years in data engineering, analytics architecture, machine learning engineering, or technical pre-sales.
• 2+ years supporting enterprise analytics or AI initiatives in a consultative environment.
• Proven track record supporting or closing enterprise-scale data & AI deals with organizations of 1,000+ employees.
• Hands-on experience designing and scoping cloud-native data platforms, pipelines, and ML solutions.
• Deep expertise in the following domains:
• Data engineering & pipeline design
• Cloud data platforms (Snowflake, Databricks, BigQuery, Synapse, Redshift)
• MLOps, model deployment & monitoring
• Real-time analytics (Kafka, Kinesis, Pub/Sub)
• Enterprise data governance & cataloging

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Technical Knowledge & Proficiencies

Category

Preferred

Cloud Data Platforms

Snowflake, Databricks, BigQuery, Synapse, Redshift

Data Architectures

Lakehouse, ETL/ELT, CDC, dimensional modeling

AI/ML

MLflow, Spark ML, Python ML libraries, LLM integration, vector databases

MLOps & Automation

CI/CD for ML, feature stores, model registries

BI & Visualization

Power BI, Tableau, Looker (architecture-level proficiency)

Security & Governance

Data classification, encryption, IAM/RBAC, lineage tools

Estimation

T-shirt sizing, story points, ROM vs. detailed BOM

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Certifications Desired

• Snowflake SnowPro Advanced, Databricks Data/ML Engineer, or equivalent
• AWS, Azure, or GCP Data Engineer / AI Engineer certifications
• Machine Learning certifications such as AWS ML Specialty, Azure AI Engineer Associate, or Google Professional ML Engineer

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Soft Skills & Competencies

• Exceptional storytelling and presentation skills—able to simplify complex data and AI concepts for executive audiences.
• Strong business acumen—understands how analytics and AI drive P&L, operational efficiency, and competitive advantage.
• Consultative discovery skills with ability to uncover unstated data and AI needs.
• Comfortable engaging with CIO, CDO, CTO, VP of Data, and senior IT leadership.
• Ability to defend architectural decisions while maintaining strong customer relationships.

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Education

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or related technical field preferred
• MBA or business-oriented coursework a plus