About this opportunity
Insight Global lists this Sr Domains Sales Engineer - Data opportunity in seattle, Washington. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
In order to make an application, simply read through the following job description and make sure to attach relevant documents.
This role sits at the intersection of:
Domain Consulting (Data & Analytics)
Pre-Sales / Sales
Data Architecture / Hands-on Engineering
KEY RESPONSIBILITIES
Domain-Led Consulting
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
Lead technical scoping, architecture definition, and solution design
Create POCs, demos, or pilot solutions — reference pipelines, lakehouse prototypes, migration assessments — to prove feasibility and value
Support proposal development, pricing inputs, and deal strategy
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
Provide architectural guardrails, reference patterns, and context for execution teams
In some cases, stay engaged in early delivery phases to stabilize outcomes
REQUIRED QUALIFICATIONS
Domain Expertise
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., BCBS 239, GDPR/CCPA, PCI-DSS).
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; product and usage analytics in Technology. xhqgsiq
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
5+ Years of experience in:
Customer-facing Solution Engineering
Technical consulting or data product engineering
Experience with rapid-prototyping or accelerators for deals
Selling through differentiated technical solutioning
Data Architecture / Hands-on Engineering
Strong hands-on experience in:
Cloud platforms (Azure, AWS, GCP) and their native data services
SQL and Python; distributed processing frameworks (Spark) and query optimization
APIs, microservices, integration patterns, and event-driven data movement
Data platforms (ETL, streaming, analytics); platform engineering and legacy data warehouse modernization
Working knowledge of:
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
PREFERRED QUALIFICATIONS
12+ years of technical and domain experience
Experience in consulting firms
Prior experience in forward-deployed roles
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
Exposure to human-in-the-loop systems and compliance workflows
Experience with Agent lifecycle management and agentic ecosystem governance, including data access controls for AI agents
Demonstrated problem-solving mindset with strong thought leadership and execution; able to structure ambiguous problems and drive solutions from concept to production across complex stakeholder environments
Worksite address
seattle, WA, 98115, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.