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Our field sales professionals rely on proactive technical support during the sales process – and our expert Systems Engineering team always steps up to the mark. We lead the development and implementation of complex and specialized products, applications, services and solutions. From delivering sales presentations and product demonstrations, to developing detailed installation or system integration plans, we ensure customers get the innovative, relevant, interoperable solutions they need.
Job Responsibility:
Build trusted relationships with customers and lead discovery to understand their goals, challenges, and technical needs.
Design and validate modern cloud architectures for data, analytics, and AI/GenAI/RAG solutions.
Create clear solution designs, proposals, and technical materials that guide customers through their Data & AI transformation.
Deliver impactful demos and PoCs that highlight value, feasibility, and business outcomes.
Communicate complex data and AI concepts in a simple, customer-friendly way to both technical and non-technical stakeholders.
Evaluate modern data and AI platforms and explain how our solutions stand out in the competitive landscape.
Requirements:
Strong customer-facing communication & storytelling: ability to explain complex topics clearly to technical/non‑technical audiences
delivering presentations to senior stakeholders (CIO/CDO)
Discovery, requirements analysis & POC excellence
Skilled at uncovering business drivers, structuring PoCs, defining success criteria, and demonstrating solutions that showcase customer value
5+ years in a customer‑facing technical role such as Sales Engineer, Solutions Architect, Data Engineer, Analytics Consultant, or Data Scientist with strong commercial exposure
Track record delivering modern Data & AI solutions
Experience in cloud DWH/lakehouse migrations, BI modernization, GenAI/RAG implementations, streaming analytics, or advanced analytics programs
Technical Skills
Hands-on experience with leading cloud & data platforms (Snowflake, Databricks, BigQuery, Redshift, Synapse, Cloudera) plus at least one major cloud (AWS, Azure or GCP)
Strong data architecture & engineering foundation
Data warehousing, data lakes/lakehouse, SQL, ETL/ELT, data modeling (star/snowflake), performance tuning, governance, and compliance (GDPR/CCPA)
Experience with data pipelines & unstructured data
Integration, orchestration (Airflow, dbt, Spark, Kafka), batch/streaming, and processing of PDFs, logs, images, text into structured/vectorized formats