Sr Domains Sales Engineer - Data

Senior Domain Sales Engineer – DataROLE OVERVIEWThe 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.

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This role sits at the intersection of:Domain Consulting (Data & Analytics)Pre-Sales / SalesData Architecture / Hands-on Engineering
KEY RESPONSIBILITIESDomain-Led ConsultingBring deep data and analytics expertise — platform, pipeline, governance, and consumption — into solution designMap client data landscape, source systems, quality constraints, and organizational realities into technical solutionsIdentify data modernization and monetization opportunities aligned to business KPIsTranslate data capability into business outcomes; build credibility with stakeholders from data engineer to CDO and executive levelAdvise on data operating models, ownership, and the sequencing of platform versus use-case investmentPre-Sales / SalesPartner with account teams to shape opportunities early / proactive business developmentTranslate client data problems into clear, differentiated solution approachesLead technical scoping, architecture definition, and solution designCreate POCs, demos, or pilot solutions — reference pipelines, lakehouse prototypes, migration assessments — to prove feasibility and valueSupport proposal development, pricing inputs, and deal strategyBuild defensible estimates for data migration and modernization work, including platform run-cost modelingData Architecture / Hands-on EngineeringRapidly prototype integrations, ingestion pipelines, data models, or analytics workflowsWork across cloud, data, API, and AI/ML domains as neededEnsure solutions are implementable, scalable, and production-ready — performant at real volumes and sustainable at real costEnsure clean handoff from pre-sales to delivery and run teamsProvide architectural guardrails, reference patterns, and context for execution teamsIn some cases, stay engaged in early delivery phases to stabilize outcomes
REQUIRED QUALIFICATIONSDomain ExpertiseThe 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. xzbcwsv 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 / Sales5+ Years of experience in:Customer-facing Solution EngineeringTechnical consulting or data product engineeringExperience with rapid-prototyping or accelerators for dealsSelling through differentiated technical solutioningData Architecture / Hands-on EngineeringStrong hands-on experience in:Cloud platforms (Azure, AWS, GCP) and their native data servicesSQL and Python; distributed processing frameworks (Spark) and query optimizationAPIs, microservices, integration patterns, and event-driven data movementData platforms (ETL, streaming, analytics); platform engineering and legacy data warehouse modernizationWorking 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 managementBI and semantic layers (Power BI, Tableau, Looker) and how consumption shapes upstream modeling
PREFERRED QUALIFICATIONS12+ years of technical and domain experienceExperience in consulting firmsPrior experience in forward-deployed rolesExposure to regulated industries or complex legacy environments (mainframe, on-premise EDW, vendor-locked source systems)Experience leading large-scale data migrations or platform consolidationsExperience operationalizing AI use cases (not just building POCs), including the data and MLOps foundations behind themExposure to human-in-the-loop systems and compliance workflowsExperience with Agent lifecycle management and agentic ecosystem governance, including data access controls for AI agentsDemonstrated 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