Summary
We are looking for a Senior Data Consultant to lead the end-to-end delivery of data and AI solutions for our clients. You will work across the full solution lifecycle, from discovery and architecture through build, deployment, and continued support.
This is a hands-on engineering role where a single engagement may require architecture design, data engineering, ML, backend development, governance, and analytics. You will own deliverables across these domains, with the latitude to make technical decisions and shape client solutions.
You are obsessed with your craft and the quality of your team's work, driving you to dive into any technical domain needed for a successful outcome.
Core Domains of Responsibility
Architecture and Strategy
Solution Architecture
- Lead architecture design for data platforms, integration layers, and AI-powered systems
- Own technical decision-making across tech stack tradeoffs, integration patterns/standards, performance targets
- Produce architecture documentation that is actionable and transferable
Data Strategy and Roadmaps
- Facilitate roadmap development sessions with client sponsors and technical teams
- Translate business priorities into phased, realistic delivery plans
- Advise on governance processes, data ownership, and platform sustainability
Pre-Sales and Proposal Development
- Collaborate with clients and technical teams to translate business needs into winning proposals
Artificial Intelligence
Large Language Models (LLMs)
- Design, build, and ship agentic workflows with production‑grade reliability, testing, and CI/CD
- Implement evaluation pipelines, guardrails, and observability tooling to ensure performance, safety, and compliance
- Take ownership of driving ROI and business impact of AI solutions
- Evaluate emerging techniques, approaches, and models to determine client fit
Machine Learning
- Partner with stakeholders to prioritize ML use cases based on business impact
- Build and deploy ML models end-to-end: scoping, feature engineering, training, validation, and production deployment
- Own model evaluation using appropriate methods (cross-validation, AUC/ROC, holdout testing); document workflows for governance and reproducibility
- Manage the model lifecycle in production: versioning, monitoring for drift, retraining triggers, and deprecation
Data Engineering
Pipelines and Integrations
- Design and implement ETL/ELT pipelines that ingest, transform, and load data from diverse source systems into target repositories
- Build new and optimize existing batch and streaming workflows, designing for performance, reliability, and data integrity
- Own documentation, error-handling standards, and handoff procedures; train client teams on maintenance best practices
API Development & Integration
- Develop and maintain APIs and backend data services that enable reliable data exchange and unlock new business capabilities
- Establish versioning, security controls, and data-contract standards to ensure consistent integration behavior
- Perform API performance testing and monitoring; guide stakeholders on consuming APIs effectively
Data Modeling and Quality
- Lead schema mapping and data modeling sessions to translate source systems into reliable, queryable structures
- Define and implement data quality checks, validation rules, and monitoring within pipelines
Business Intelligence
Dashboards and Reporting
- Run KPI discovery sessions with business stakeholders to define measurable metrics and reporting requirements
- Build dashboards and reports in tools such as Power BI, Tableau, or Fabric that drive adoption through clarity, not complexity
- Produce data dictionaries and aggregation logic that enable client self-service over time
Analytics
- Conduct in-depth analysis to surface patterns, anomalies, and actionable insights -- going beyond surface-level reporting to inform client decisions
- Translate analytical findings into clear recommendations that account for the client's business context, constraints, and decision-making horizon
Governance
Master Data Management
- Design data governance frameworks: ownership models, quality standards, and stewardship processes
- Lead master-data modeling sessions to define key entities, hierarchies, and golden-record rules
- Integrate governance controls into pipelines, leveraging automation and AI for effective implementation
Metadata and Documentation
- Implement metadata practices that inventory data assets, tag lineage, and maintain business glossaries
- Ensure documentation is current, transferable, and does not live only in one person's head
Long-Term Support and Accountability
- Maintain accountability for delivered solutions beyond deployment -- monitoring, maintenance, and issue resolution that can extend years after initial handoff
- Provide timely explanations and reports when issues arise; conduct root cause audits and push preventive measures
Data Security and Compliance
- Apply security best practices across pipelines and integrations -- including access controls, encryption, secrets management, and role-based least-privilege access, drawing on established frameworks (e.g., OWASP SAMM) where appropriate
- Design with security in mind at every stage, particularly in client environments with sensitive or regulated data
Emerging Domains and Continuous Evolution
- Stay ahead of the rapidly changing landscape of advanced analytics, AI, and data strategy
- Research, adopt, and pilot new tools, technologies, and methodologies that extend the value of data consulting engagements
- Maintain versatility by continuously expanding breadth and depth across new and emerging domains, ensuring QADworks delivers future-ready solutions to clients
Who We're Looking For
For this role, we're looking for someone who has the following:
Proven hands-on engineering expertise and execution
- Demonstrated success delivering production data pipelines, ML models, agentic workflows, and backend applications
- Hands-on experience with Python, SQL, and the Microsoft data platform (including Microsoft Fabric, Azure Data Factory, Azure SQL, and all Azure AI services)
- Proficiency with transformation and orchestration tooling (Fabric Pipelines, Azure Data Factory, dbt) and CI/CD practices using Azure DevOps or equivalent
Quality-driven mindset
- Innate drive to constantly put deliberate attention and care in getting things right
- Holds an extremely high level of self and team accountability
- Defines and upholds standards; doesn't treat governance, security, or documentation as someone else's job
- Documents work, transfers knowledge, and doesn't hoard context
Client-facing delivery experience
- Has worked directly with clients as the primary interface
- Can lead discovery sessions, present architecture decisions, and communicate technical tradeoffs to non-technical audiences without losing substance
- Maintains full situational awareness across engagements (status, dependencies, risks, and downstream effects) and raises them without being asked
- Can scope work that isn't fully defined, identify risks early, and make reasonable technical decisions without waiting for direction
- Has seen enough engagements to know what tends to go wrong and how to prevent it
Clear, transparent communication
- Engages teammate and clients with clarity, transparency, and trust
- Comfortable with objective, even uncomfortable, discussions, seeing disagreements as opportunities to learn, broaden perspective, and refine approaches
- Understands Western business norms and mindsets
- Adapts communication and approaches to fit client culture
- Balances communication with empathy and gratefulness
- Embodies Death by Meeting principles
High agency
- Ability to come up with initiatives and own the outcome
- Manages their own allocations, capacity, and commitments
- Assemble the right people for project execution
- Makes practical adjustments to mitigate risk rather than absorbing quietly
Resiliency with a growth mindset
- Applies a just approach to errors by focusing on learning, refinement, and prevention
- Thrives under pressure and keeps day-to-day operations running when stress or unforeseen events occur
- Practices succession planning by documenting work and mentoring backups
- Regularly self-assesses personal impact, relationships, and alignment with mission
Adaptability and constant preparedness
- Continuously scans for emerging trends and subtle cues
- Treats plans as hypotheses and runs small pilots to validate ideas
- Quickly pivots based on priorities and criticalities
Ownership of their work throughout its full lifecycle
- Maintains quality and composure through complexity and shifting priorities
- Ability to be hands-on in all phases from pre-sales to user training
- Engage in sales calls and project management responsibilities as needed
- Proactively identifies and closes gaps, ensuring issues are fully resolved
