Data Ideology, LLC

• 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. • Fine-tuning or custom model training is not a default commitment in this phase — adaptation approach will be determined based on discovery findings. • 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. • Clearly distinguish confirmed decisions from those that remain conditional on hardware specifications or interface access not yet confirmed.

• Assess target edge hardware against the requirements of an on-device inference loop: evaluate processor architecture, available memory, OS and runtime environment, and whether candidate edge runtimes (such as IoT Greengrass or equivalent) can be supported. • Evaluate candidate edge inference frameworks for CPU-only SLM deployment — including TensorFlow Lite, ONNX Runtime, llama.cpp, and equivalents — assessing quantization approaches, inference latency, and memory footprint against feasibility targets confirmed during discovery. • Assess real-time data ingestion feasibility from operational subsystem interfaces, evaluating candidate patterns for consuming concurrent data streams within the memory and compute constraints of the target hardware. • Design and evaluate local data store options for the on-device SLM context, including storage formats, retrieval latency, and update mechanisms appropriate for the edge environment. • Build a constrained feasibility demonstrator on laptop or workstation hardware using simulated data feeds. • Implement a small number of scoped interaction flows in the demonstrator, integrating the voice interface pipeline with the SLM inference and local data retrieval components as agreed through the engagement scope. • Collaborate with the AI/ML Architect on SLM selection, domain restriction approach, and inference pipeline design — providing hardware and runtime constraint inputs that shape what is architecturally feasible. • Collaborate with the AWS Solutions Architect on the edge-to-cloud data channel, identifying what can realistically be buffered and transmitted from a constrained edge device under variable connectivity conditions. • Document hardware assessment findings, framework evaluations, and architectural trade-offs as Architecture Decision Records (ADRs) with explicit rationale. • Clearly flag where recommendations are conditional on hardware or interface specifications not yet confirmed. • Communicate technical constraints and feasibility findings clearly to both technical architects and non-technical client stakeholders throughout the engagement.

• Lead structured discovery workshops with client engineering and product leadership. • Compile and maintain detailed requirements matrices documenting data types, system interfaces, processing frequencies, and dependency classifications. • Own scope tracking and MVP boundary management. • Consolidate and maintain business glossaries, metadata standards documentation, and data governance artifacts. • Act as the coordination bridge across concurrent workstreams. • Facilitate technical feasibility workshops, track open questions and unresolved dependencies, and escalate risks to the project lead. • Translate complex technical architecture concepts into clear written requirements. • Support the Program Manager in maintaining decision logs, action item trackers, risk registers, and stakeholder communication cadence. • Produce clear, structured written artifacts throughout the engagement. • Assist with light project management responsibilities including milestone tracking, status reporting, and coordination of cross-team dependencies.

Title: Data Engineer Location: Remote - USA Full Time Mid Level Job Description: Data Ideology At DI, we provide Data & Analytics expertise to drive measurable business outcomes, often solving complex business problems for our clients. Our data analytics advisory services enable our customers to transform data into insights by driving a culture of empowerment and ownership of results. Our team consists of highly motivated individuals passionate about learning, understanding, collaborating, and intellectually curious. Data Engineer We are looking for a Data Engineer to join our growing team. Data Engineer will leverage their business and technical knowledge to develop production-ready data models by integrating multiple data sources while working with business and technical teams to understand business strategy and objectives, gather information, and ensure business requirements are being fulfilled throughout the entire data & analytics lifecycle. Responsibilities To perform in this position successfully, an individual must be able to perform each essential duty satisfactorily. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Other duties may be assigned to meet business needs. - Ability to collect and understand business requirements and translate those requirements into an actionable data warehouse plan. - Knowledge of multi-dimensional and tabular design patterns and ability to identify solutions that leverage these modeling techniques. - Ability to work within the SDLC framework in multiple environments and understand the complexities and dependencies of the data warehouse built within those constraints. - Ability to define and implement best practices across database design and ETL. - Ability to direct the work of others, including but not limited to directing ETL development, demonstrating an understanding of key concepts of ETL/ELT, including best practices for optimization and scheduling. Supervisory Responsibilities: None Qualifications Education and Experience: - Proven understanding of Data Warehousing, Data Architecture, and BI. - Experience with data pipelines and architecture/engineering. - Knowledge of modern apps and data platforms. - Cloud-based project implementation. - Python or Java experience is a plus Knowledge, Skills, and Abilities: - BI/Data Warehousing (4+ years) - Cloud platforms (2+ years) - ETL (4+ years) - SQL (4+ years) - Data Modeling - Data Vault Modeling - Healthcare experience a plus Work Environment: - Remote work from home. - Hours of work and days are generally Monday through Friday. Specific business hours will depend on client needs. Physical Demands: - Must be able to remain in a stationary position 50% of the time. - The person in this position must occasionally move about inside the office to access file cabinets, library stacks, office machinery, etc. - Constantly operates a computer and other office productivity machinery, such as a calculator, copy machine, and printer. - The person in this position frequently communicates with clients and coworkers. Must be able to exchange accurate information in these situations. Benefits:Eligible for Medical, Dental, Vision, 401(k), training and certification reimbursement, and flexible time off in accordance with company policies.

• Partner with business stakeholders, architects, and engineering teams to understand customer needs, business priorities, and technical constraints. • Translate business requirements into actionable user stories, technical requirements, acceptance criteria, and delivery-ready backlog items. • Own and manage the product backlog, ensuring prioritization of the highest-value initiatives aligned with business goals and technical dependencies. • Participate in all phases of the product lifecycle, including discovery, roadmap planning, requirements gathering, development, testing, release, and adoption. • Collaborate with engineering and technical teams to define scalable solution designs, integration requirements, API dependencies, data flows, and system impacts. • Facilitate Agile ceremonies including backlog refinement, sprint planning, stakeholder reviews, and release readiness activities. • Develop KPIs, success metrics, and business analytics to measure product adoption, performance, and ROI. • Continuously gather stakeholder and customer feedback to improve product capabilities and delivery outcomes. • Identify risks, dependencies, and opportunities while helping guide product decisions and delivery execution.

• 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

• Lead and independently manage the full lifecycle of a data governance engagement • Facilitate stakeholder interviews, workshops, and working sessions to gather requirements and drive decisions • Assess current-state data management practices across systems, files, and spreadsheets • Design and implement data classification frameworks (e.g., Public, Internal, Confidential, Restricted) • Develop actionable data handling guidelines, including access, storage, and sharing standards • Define data lifecycle policies, including retention and deletion practices • Establish data ownership and stewardship models with clear accountability • Identify and remediate data risks such as duplication, inconsistent definitions, and uncontrolled data sharing • Develop and deliver a clear, phased data governance roadmap with prioritized recommendations • Produce high-quality, client-ready deliverables including policies, frameworks, and presentations • Provide guidance and thought leadership to stakeholders throughout the engagement

• Support the design and implementation of strategies that emphasize data ownership, quality, and lifecycle management • Lead AWS platform design (VPC, IAM, networking) and define S3 storage patterns across Bronze, Silver, and Gold medallion layers • Author production-ready Terraform modules with state management and remote backends • Ensure strict tenant isolation across storage and compute layers • Serve as the primary technical authority for all cloud infrastructure decisions • Define and document reference blueprints, CDC/streaming partitioning strategies, operational runbooks, cost-tagging standards, and CloudWatch observability patterns

• Facilitate stakeholder meetings, workshops, and working sessions to gather requirements and support governance initiatives • Assess how data is stored, used, and managed across systems, files, and spreadsheets • Develop and implement data classification frameworks (e.g., Public, Internal, Confidential, Restricted) • Define and document data handling guidelines, including access, storage, and sharing protocols • Contribute to data lifecycle management policies, including retention and deletion standards • Help establish and support data ownership and stewardship roles across the organization • Identify and mitigate common data risks such as duplication, inconsistent definitions, and uncontrolled sharing • Support development and execution of phased data governance roadmaps • Create documentation, policies, and training materials to support adoption • Collaborate with internal teams to ensure consistent and high-quality implementation of governance practices

• Conduct in-depth analysis using the data warehouse to identify trends, patterns, and insights. • Interpret complex data sets and communicate findings to both technical and non-technical stakeholders. • Assist with ETL processes to extract, transform, and load data from various sources. • Collaborate with IT teams to create seamless data integrations. • Perform thorough data validation and testing to ensure the highest possible data quality. • Clearly define and communicate when quality standards are not being met. • Perform root cause analysis to identify the underlying or fundamental causes of an issue or problem. • Work closely with cross-functional teams, including IT and business stakeholders, to align on goals and key deliverables. • Accurately document data mappings and definitions.