Togetherwork

Consultant

• Lead customer onboarding engagements in alignment with defined delivery frameworks, timelines, and scope
• Configure SaaS applications based on documented customer requirements and established best practices
• Guide customers through setup, validation, and go-live readiness activities
• Identify onboarding risks, dependencies, and issues; escalate appropriately to maintain delivery momentum
• Deliver end-user and administrator training to support successful adoption
• Provide onboarding guidance, best practices, and product usage recommendations
• Ensure customers are prepared for transition to Customer Success or Support teams
• Maintain accurate onboarding documentation, configuration records, and customer artifacts
• Track progress against milestones, timelines, and success criteria
• Collaborate with Project or Engagement Managers to support planning, coordination, and status reporting
• Leverage approved AI tools and automation to improve onboarding efficiency, quality, and consistency
• Use data, dashboards, and AI-supported insights to monitor onboarding health and identify risks early
• Continuously build AI literacy and apply AI-enabled practices as part of day-to-day delivery

Onboarding Consultant

Data Engineer

• Conduct client data readiness assessments, evaluating source systems, data quality, and migration complexity during early implementation phases.
• Define data cleansing, transformation, and validation frameworks to improve migration success rates.
• Partner with Implementation Project Managers and Solution Consultants to ensure data architecture supports predictable delivery timelines.
• Establish standardized migration playbooks, mapping templates, and reusable ETL frameworks to accelerate onboarding and reduce implementation risk.
• Design and maintain conceptual, logical, and physical data models within Microsoft Dynamics 365 (Protech).
• Define and document data architecture standards, integration patterns, and governance best practices.
• Architect and oversee ETL processes using SSIS and other standard ETL tools.
• Design and implement integrations leveraging KingswaySoft and/or Pentaho Data Integration.
• Lead data migration strategy and execution support for onboarding and implementation projects.
• Identify data dependencies, risks, and integration gaps early in the delivery lifecycle.
• Ensure secure, scalable, and high-performance data exchange between D365 and external systems.
• Collaborate with Product and Engineering to assess the architectural impact of platform enhancements.
• Partner with Analytics teams to ensure reporting accuracy, consistency, and data integrity.
• Develop reusable integration templates, migration accelerators, and standardized data patterns.
• Proactively identify systemic data design issues and recommend improvements to reduce rework and onboarding delays.
• Utilize AI-enabled tools to analyze source system data structures, identify data quality risks, and support scalable migration and integration architectures.
• Maintain clear documentation of data flows, mappings, transformations, and integration logic.

Data Architect

Software Engineer

• Togetherwork is seeking a Principal Architect, AI & Developer Productivity to own how AI accelerates the software development lifecycle across the portfolio. This is a hands on leadership role for someone who has shipped AI augmented engineering tooling at scale and can prove measurable improvements in developer throughput, software quality, and cycle time.
• You will define and operationalize the AI assisted development stack across the organization: IDE assistants, code review automation, test generation, security scanning, documentation, and release automation. You will set the standards, build the platform, and drive adoption across product teams. You will measure outcomes against DORA metrics and retire tools that do not produce returns, regardless of how fashionable they are.
• Define and operationalize the AI assisted engineering platform across the portfolio, covering IDE assistants, agentic coding tools (Claude code, cursor, etc), code review automation, test generation, security scanning, documentation, and release automation.
• Architect a model and vendor agnostic abstraction layer so the organization is not locked into a single tool, model, or provider as the landscape evolves monthly.
• Establish reference architectures and golden paths for AI augmented workflows that teams can adopt without forcing a single stack across all products.
• Establish acceptable use, IP protection, intellectual property leakage prevention, secret scanning, and data exfiltration controls for AI in the SDLC.
• Implement open source license scanning to prevent contamination from AI generated code that reproduces GPL, AGPL, or other restrictive license material.
• Define audit trail and traceability standards: which AI tool wrote what code, what tests were generated, what was reviewed, what was approved.
• Partner with Security, Legal, Compliance, and Risk to embed SOC 2, PCI, PII, SOX, data residency, and other regulatory requirements into the platform design.
• Support audit and risk assessment readiness by ensuring platform documentation, logs, and controls meet enterprise and regulatory expectations.
• Embed AI driven capabilities into CI/CD: automated pull request review, test synthesis, flaky test triage, vulnerability remediation, intelligent rollout, and incident analysis.
• Establish quality gates for AI generated code including coverage, mutation testing, security scanning, and license compliance before merge.
• Lead enablement across product teams: onboarding paths, paved roads, internal developer portal capabilities, and training for AI assisted workflows.
• Treat developer experience as a product with clear roadmaps, success metrics, user research, and feedback loops.
• Distinguish real productivity from the illusion of productivity. AI tools inflate volume metrics without necessarily delivering value, and traditional metrics like commits and lines of code are unreliable in AI native workflows.
• Report tool cost against measured outcomes. Make kill, scale, or replace decisions on tools that do not return $2 to $3 of value for every $1 of cost.
• Maintain an evaluation harness so new tools can be benchmarked against incumbents on real internal work, not vendor demos.
• Evaluate and select tooling across the current market: GitHub Copilot Enterprise, Cursor, Claude Code, and emerging entrants. Negotiate enterprise terms in partnership with procurement.
• Make defensible build vs buy decisions on AI components, frameworks, and pipeline integrations based on cost, security posture, switching cost, and outcomes.
• Stay current on emerging tools and models. Recommend platform evolution quarterly rather than annually. The field moves monthly.
• Bring acquired engineering teams onto the standard AI augmented SDLC platform with a clear runbook for tooling rationalization.
• Evaluate acquired company SDLC tooling and provide structured recommendations on what to integrate, rationalize, or retire.
• Own the total cost of AI in the SDLC: license consumption, token spend, infrastructure, and developer time. Implement chargeback, cost ceilings, observability, and alerting.
• Manage token spend at scale.
• Build cost models for new tool rollouts that include training, change management, and ongoing platform support, not just license fees.
• Partner with engineering leaders, product, security, legal, and procurement to align platform direction with business strategy.
• Mentor senior engineers and engineering managers on AI assisted development patterns and the discipline required to use them effectively.
• Communicate architecture decisions, trade offs, and platform outcomes clearly to executive stakeholders including the CTO and CFO.

Onboarding Consultant

Data Architect

Principal Architect, AI – Developer Productivity