• Act as the technical engine behind the transformation of Finance function • Design, build, and embed a real-time, self-service reporting layer • Systematically eliminate capability debt and manual reconciliation • Build an advanced profitability attribution engine and deploy ML/AI techniques for automated forecasting • Establish auditable reconciliation frameworks between new reporting layer and source systems

• Develop and maintain data integrations between SAP and Databricks • Work on data modeling and data transformations for analytical consumption • Build and maintain reporting and BI structures • Support business requests related to data and analytics • Collaborate with international teams to define and implement solutions

Role Description Vantaca IQ is the intelligence layer at the core of Vantaca’s platform, and this team builds it. You take messy, multi-tenant operational data, organize it, and turn it into the insights and signals the platform acts on — surfaced where decisions get made and served to HOAi agents that act on it. You own the stack that makes that possible: - Ingestion and pipelines - Schemas and aggregation beneath every report - Reporting and ad-hoc query surfaces - Derived-intelligence layer - Semantic and ontological model on top IQ is data-as-a-service, one governed source feeding humans and agents where they are — proactive, personalized, channel-agnostic. The hard problems sit here: - Serving cross-tenant data without leaking a byte - Semantic governance that keeps one number from forking into five - Eval harnesses that prove a grounded LLM is right - Pipelines trustworthy enough to bill against and report to boards What the Team Owns - Ingestion & pipelines: batch and change-data-capture out of the operational system of record; transformations, scheduling, backfills, and idempotent reprocessing. - Data models & metric layer: relational and dimensional schemas, and define-once metrics with thoughtful governance. - Aggregation, rollups & derived metrics: materializations, incremental rollups, and partitioning/indexing for fast, cheap queries — plus the score and benchmark jobs, with data-quality checks, lineage, and tests that keep them correct. - Multi-tenant isolation: tenant-scoped access and row/column controls on every surface, including cross-tenant aggregates that expose no individual tenant's data. - Access & serving layer: how every consumer reads the platform's intelligence — query and metrics APIs, an MCP server for agents and external tools, embeddable feeds, and push to Teams and Slack — one governed contract whether the caller is a person, an app, or an agent. - Reporting & ad-hoc surfaces: report definitions, board-packet generation, and self-serve query interfaces. - LLM grounding & evals: the retrieval/semantic interface an LLM queries against, and the eval harnesses that measure and gate accuracy. - Signal & action contracts: insight-detection jobs and the typed, audited output contracts HOAi agents consume to act. What We're Looking For - Data fundamentals: you've built pipelines or aggregation layers over messy operational data and understand how analytical engines execute your queries. - Modeling instinct: relational and dimensional modeling, and the conviction that every metric you publish is an interface someone, or some agent, depends on. - Product sense for data: you care that an insight changes how our users do business. - AI-native by default: Active daily use of AI coding agents (Claude Code, Cursor, or similar); this is a baseline, not a differentiator. - Polyglot and pragmatic: You reach for the right tool to solve each problem, while thoughtfully evolving the ecosystem. - Curiosity and ownership: you run at ambiguous problems and take systems end-to-end (build → ship → operate). - Source-of-truth scar tissue: you've built a data platform other teams trusted and kept it trustworthy as it grew. - Semantic governance: you've defined semantic-layer or metric-governance architecture and lived with the consequences. - Grounded-LLM receipts: you've shipped or evaluated an LLM system grounded in governed data and can explain what you measured. - Raising the bar: testing strategy, documentation, and mentoring. Nice to Have - Semantic-layer or metrics-store experience (e.g., MetricFlow, LookML, Cube, dbt, or similar). - Modern data stack exposure (e.g., warehouses, lakehouses, event streaming, and orchestration tools). - Multi-tenant analytics or cross-customer benchmarking — serving derived intelligence without leaking anyone's data. - Exposure to property management, HOA, or real estate tech — helpful but not required. Core Values - Always Growing: Likes change and enjoys finding new ways to improve their knowledge and the platform. Always ready to learn quickly about emerging AI technologies and infrastructure patterns, helping themselves and the team grow. - Win as a Team: Builds trust and works together by making sure everyone communicates well. Actively involved in daily platform work, working closely with engineering teams, listening to their infrastructure needs, and celebrating technical successes together. - Accountability Starts with Me: Notices platform problems and takes personal action to solve them. Takes ownership of platform performance, reliability, and the success of internal customers building on the platform. - Unwavering Commitment to Customer Experience: Regularly talks to internal customers (product teams, engineers), taking personal responsibility to understand what they need from the platform, address infrastructure concerns, and make their development experience better with improved platform capabilities. - Innovate Boldly: We challenge the status quo and push technical boundaries to create meaningful change. We act with urgency and purpose, knowing that platform innovation drives our AI-native product success. Benefits - Medical, Dental, and Vision kick in day one - Unlimited PTO (with a requirement for employees to take a minimum of one continuous week per year) - 401K with Company Match - Remote Flexible - come to the office when needed - Great parental leave benefits - Named on Inc 5000 list of America's Fastest Growing Private Companies - Named on Inc 5000 Vet 100 Private Companies list multiple years in a row - Winner of Coastal Entrepreneur Award, Technology Category - Active employee-led Culture Committee - Ongoing industry and professional development trainings available to all employees - Multiple leaders on the executive committee recognized as 40 under 40 recipients for contributions to business and community Why You Should Join Our Team - AI-First Product Culture - Build the intelligent infrastructure that powers AI innovation at scale - Our eNPS is +68! (Google it, that is great)

Role Description We are sharing a specialised remote consulting opportunity for experienced data engineers with strong coding agent experience, practical data infrastructure judgment, and the ability to evaluate complex data engineering implementations across realistic technical scenarios. This role supports current and upcoming remote consulting opportunities focused on data engineering evaluation, coding-agent-assisted technical workflows, pipeline assessment, data platform review, and distributed data system analysis. Selected professionals may use tools such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable coding agents to complete, review, and evaluate data engineering tasks involving ETL pipelines, data warehouses, analytics platforms, distributed systems, and large-scale data infrastructure. Key Responsibilities - Data Engineering Evaluation - Use modern coding agents to complete and evaluate complex data engineering tasks - Review generated implementations involving ETL pipelines, data warehouses, analytics platforms, and distributed data systems - Assess technical outputs for correctness, scalability, maintainability, reliability, and production-readiness - Apply professional data engineering judgment to realistic infrastructure and pipeline scenarios - Pipeline & Data Platform Review - Evaluate pipeline architecture, data transformation logic, ingestion workflows, orchestration patterns, and data quality checks - Review data warehouse and analytics platform implementations for performance, accuracy, structure, and maintainability - Identify bugs, edge cases, scalability issues, failure modes, and weak assumptions in data engineering outputs - Provide structured feedback on data flow, system design, reliability, and implementation quality - Coding Agent Output Assessment - Compare outputs from multiple coding agents and assess their strengths, weaknesses, accuracy, and practical usefulness - Identify where generated solutions succeed, where they fail, and where additional engineering judgment is required - Evaluate whether generated data infrastructure reflects real-world data engineering standards - Document technical review findings clearly for project teams and quality evaluation workflows - Technical Documentation & Feedback - Produce clear, structured evaluations of data engineering tasks and generated outputs - Explain reasoning around pipeline design, data modelling, warehouse architecture, distributed systems, scalability, and failure handling - Support technical assessment workflows by documenting accepted work, improvement areas, and practical engineering conclusions - Help ensure outputs reflect production-scale data engineering expectations Qualifications - 2+ years of professional data engineering experience - Hands-on experience building ETL pipelines, data warehouses, analytics platforms, distributed data systems, or large-scale data infrastructure - Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable tools - Ability to evaluate generated data infrastructure and pipeline implementations for correctness, scalability, and reliability - Experience supporting large-scale data platforms is strongly preferred - Strong understanding of data modelling, data quality, orchestration, distributed processing, warehouse design, and pipeline maintainability - Clear written communication skills and comfort documenting technical reasoning in a remote, project-based environment Educational Background - A degree in Computer Science, Data Engineering, Software Engineering, Computer Engineering, Information Systems, Statistics, or a related technical field is helpful - Equivalent professional experience in data engineering, analytics engineering, distributed systems, or production data platforms is also highly relevant Nice to Have - Experience with Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Snowflake, BigQuery, Redshift, Databricks, or comparable data tools - Familiarity with cloud data platforms, data lakehouse architecture, orchestration systems, batch processing, streaming pipelines, or data quality frameworks - Experience with CI/CD workflows, Docker, Kubernetes, Terraform, observability tooling, or infrastructure automation in data environments - Background in technical code review, data architecture review, pipeline performance evaluation, or large-scale analytics systems - Strong comfort working in sprint-based project environments with focused technical assessment windows Why This Opportunity - Remote consulting work aligned with data engineering, coding agent, and technical evaluation expertise - Opportunity to evaluate realistic data engineering workflows involving ETL pipelines, data warehouses, analytics platforms, and distributed data systems - Suitable for engineers who enjoy technical assessment, tool-assisted coding workflows, pipeline review, and practical data infrastructure problem-solving - Sprint-based project work that can align with focused availability and remote schedules Contract Details - Independent contractor engagement - Fully remote and flexible scheduling - Sprint-based, project-based availability - Some project work may run in focused 12–24 hour sprint windows depending on project requirements - Compensation may reach up to $90/hour, depending on project scope, experience, and accepted work structure - Some projects may use accepted-task compensation depending on the specific workflow - Payments are made weekly via Stripe or Wise based on services rendered - Projects may be extended, shortened, adjusted, or concluded based on project needs and performance - Candidates requiring H1-B or STEM OPT sponsorship support are not eligible at this time - Work must not involve sharing confidential or proprietary information from any employer, client, or institution About the Platform This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams. By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy