Data Ideology, LLC

Support Engineer

• Lead structured discovery workshops with client engineering and product leadership
• Own scope tracking and MVP boundary management
• Consolidate and maintain business glossaries
• Act as the coordination bridge across concurrent workstreams
• Facilitate technical feasibility workshops
• Translate complex technical architecture concepts into clear written requirements
• Support the Program Manager in maintaining decision log, action item tracker, risk register, and stakeholder communication cadence
• Produce clear, structured written artifacts throughout the engagement
• Assist with light project management responsibilities

Technical Business Analyst

Data Engineer

• Design and build scalable, secure, and cost-effective data solutions in Snowflake.
• Develop and optimize data pipelines using tools such as dbt, Python, CloverDX, and cloud-native services.
• Participate in discovery sessions with clients to gather requirements and translate them into solution designs and project plans.
• Collaborate with engagement managers and account teams to help scope work and provide technical input for Statements of Work (SOWs).
• Serve as a Snowflake subject matter expert, guiding best practices in performance tuning, cost optimization, access control, and workload management.
• Lead modernization and migration initiatives to move clients from legacy systems into Snowflake.
• Integrate Snowflake with BI tools, governance platforms, and AI/ML frameworks.
• Contribute to internal accelerators, frameworks, and proofs of concept.
• Mentor junior engineers and support knowledge sharing across the team.

Senior Data Engineer, Snowflake

AI Engineer

• Lead SLM candidate evaluation and selection: assess Small Language Model options for edge deployment against hardware constraints, inference latency requirements, domain restriction feasibility, and licensing.
• Produce a technology assessment with explicit trade-off rationale and a recommended approach.
• Design the domain restriction and guardrails architecture: define how the SLM is constrained to a known operational scope, how out-of-domain responses are prevented, and how the system enforces retrieval-first, non-authoritative behavior appropriate for a safety-adjacent environment.
• Design the capability framework that structures how the system responds to operator queries — how capabilities are scoped and isolated, how the framework supports incremental addition of new interaction types over time, and what the prototype will implement.
• Design the retrieval-augmented inference pipeline: define how the SLM retrieves context from a local knowledge store at inference time, including retrieval strategy, context injection approach, and latency budget appropriate for the edge environment.
• Evaluate candidate cloud services for knowledge retrieval, model governance, and fleet-level model lifecycle management including over-the-air model distribution to edge devices.
• Produce architecture recommendations aligned to client enterprise standards; all service selections are subject to client review and approval.
• Define the offboard ML lifecycle: how models are evaluated, adapted through prompting and retrieval augmentation, versioned, governed, and distributed at scale.
• Collaborate with the Edge ML / Embedded Engineer on hardware constraint inputs that shape SLM selection and inference pipeline design, ensuring architecture recommendations are grounded in confirmed runtime feasibility.
• Collaborate with the AWS Solutions Architect on candidate cloud service architecture for model governance, knowledge retrieval, and the model update pipeline, ensuring the cloud-side AI architecture aligns with the broader platform.
• Document safety design principles and operational boundaries — authority separation, bounded AI behavior, explainability approach, and human-in-the-loop considerations — as architecture artifacts for client engineering and compliance review.
• Produce all architecture recommendations as Architecture Decision Records (ADRs) with explicit trade-off rationale.

Senior AI/ML Architect

Cloud Engineer

• Own the architecture for cloud-native data platforms, including Lakehouse designs (Delta Lake, Iceberg, Databricks, or Microsoft Fabric) that support reporting, analytics, and BI
• Design scalable data models supporting enterprise platforms, including ERP, financial, operational, and project management systems
• Lead modernization initiatives for legacy data environments, including migration strategy, data mapping/transformation, reconciliation, and cutover to production
• Build frameworks for real-time and near real-time data ingestion and processing, alongside batch pipelines (ELT/ETL)
• Drive decisions across storage, orchestration, data quality, reliability, and performance
• Define semantic data models that enable self-service analytics and consistent reporting, in coordination with data governance, metadata management, and master data management (MDM) standards
• Partner with business and technical stakeholders across functional areas to prioritize requirements and translate them into architectural decisions
• Support secure, governed data access in coordination with Identity and Access Management (IAM) teams
• Evaluate emerging technologies, including AI, and identify opportunities to apply them within the data ecosystem; help shape the long-term technical roadmap
• Mentor junior architects and data management staff, and translate complex technical concepts into clear, business-friendly language for technical and non-technical audiences alike

Technical Business Analyst

Senior Data Engineer, Snowflake

Senior AI/ML Architect

Cloud Data Architect