Senior Systems Engineer - AI Data Platform

SUMMARY

Provides pre-sales technical support to field sales teams during the sales process.

Responsible for ensuring the technical validity and interoperability of the solution, as well as aligning it directly with the customers' strategic business plans.

ACCOUNTABILITIES


* 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

Technical Skills


* 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).


* Unstructured data & AI/RAG: 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).


* Analytics, BI, and data science


* 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).

Consulting Skills


* 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.


* Storytelling & communication


* Excellent at translating complex technical topics into clear, business-oriented narratives for both tec...