Sr Domains Sales Engineer - Data

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. xhyhwjd
• 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