Senior Domain Sales Engineer – Data ROLE OVERVIEW The Senior Domain Sales Engineer – Data is a hybrid domain expert, technologist, and pre-sales solution professional who works directly with clients to shape, design, and validate data solutions before they are sold and delivered. DSEs translate complex business problems into practical, scoped, and implementable data architectures — and de-risk deals by demonstrating feasibility early.
Domain Consulting (Data & Analytics) Pre-Sales / Sales Data Architecture / Hands-on Engineering
Bring deep data and analytics expertise — platform, pipeline, governance, and consumption — into solution design Map client data landscape, source systems, quality constraints, and organizational realities into technical solutions Identify data modernization and monetization opportunities aligned to business KPIs Translate data capability into business outcomes; build credibility with stakeholders from data engineer to CDO and executive level Advise on data operating models, ownership, and the sequencing of platform versus use-case investment Pre-Sales / Sales Partner with account teams to shape opportunities early / proactive business development Translate client data problems into clear, differentiated solution approaches Create POCs, demos, or pilot solutions — reference pipelines, lakehouse prototypes, migration assessments — to prove feasibility and value Build defensible estimates for data migration and modernization work, including platform run-cost modeling Data Architecture / Hands-on Engineering Rapidly prototype integrations, ingestion pipelines, data models, or analytics workflows Work across cloud, data, API, and AI/ML domains as needed Ensure solutions are implementable, scalable, and production-ready — performant at real volumes and sustainable at real cost Ensure clean handoff from pre-sales to delivery and run teams The ideal candidate is a technologist in cloud, data, API, and AI/ML, with deep expertise in at least one of Banking / Financial Services, Telecommunications, Retail / Consumer, or Technology, bringing both data architecture knowledge and hands-on industry process experience across: Data Platform & Architecture — Hands-on experience designing and building modern data platforms — lakehouse and warehouse architectures (Databricks, Snowflake, BigQuery, Synapse/Fabric), medallion or layered modeling, storage and compute separation, and cost-performance tuning at scale. Data Engineering & Integration — Practical depth in batch and streaming ingestion, ELT/ETL orchestration (dbt, Airflow, ADF, Glue), change data capture from operational systems, and real-time event pipelines (Kafka, Kinesis, Event Hubs). Data Governance & Quality — Working command of data quality frameworks, lineage and cataloging, master and reference data management, privacy and consent handling, and the regulatory obligations relevant to the candidate's industry (e.g., Industry Data Domains — Understanding of the analytical and operational use cases that drive value in the candidate's industry — for example risk, fraud, AML and customer 360 in Banking; network, subscriber and churn analytics in Telco; demand forecasting, pricing, assortment and personalization in Retail; Analytics & AI Enablement — Applied experience preparing data for machine learning — feature engineering and feature stores, vector stores and retrieval patterns for GenAI, and the data foundations required to put models into production. Pre-Sales / Sales Customer-facing Solution Engineering Technical consulting or data product engineering Data Architecture / Hands-on Engineering Cloud platforms (Azure, AWS, GCP) and their native data services SQL and Python; APIs, microservices, integration patterns, and event-driven data movement Data platforms (ETL, streaming, analytics); platform engineering and legacy data warehouse modernization AI/ML / GenAI use cases (not theoretical — applied understanding) Modern architecture patterns (event-driven, distributed systems, data mesh and domain-oriented ownership) DataOps practices — CI/CD for data, testing, observability, and environment management BI and semantic layers (Power BI, Tableau, Looker) and how consumption shapes upstream modeling
Exposure to regulated industries or complex legacy environments (mainframe, on-premise EDW, vendor-locked source systems)
Experience leading large-scale data migrations or platform consolidations
Experience operationalizing AI use cases (not just building POCs), including the data and MLOps foundations behind them
Experience with Agent lifecycle management and agentic ecosystem governance, including data access controls for AI agents
able to structure ambiguous problems and drive solutions from concept to production across complex stakeholder environments
Sr Domains Sales Engineer - Data in seattle at Unknown Company
This position is listed as full time and hybrid.