MathCo
Website:
mathco.com
Company:
https://www.linkedin.com/company/themathcompany
Seniority: Mid-Senior level
Industries: Data Infrastructure and Analytics and IT Services and IT Consulting
Job details:
About the Company
TheMathCompany or MathCo® is a global Enterprise AI and Analytics company trusted by leading Fortune 500 and Global 2000 enterprises for data-driven decision making. Founded in 2016, MathCo builds custom AI and advanced analytics solutions to solve enterprise challenges through its hybrid model. NucliOS, MathCo’s proprietary platform, enables connected intelligence at a lower total cost of ownership (TCO).
At MathCo, we foster an open, transparent, and collaborative culture, making it a great place to work. We provide exciting growth opportunities and value capabilities and attitude over experience, enabling our Mathemagicians to 'Leave a Mark'.
We are looking for a seasoned forward-deployed data and context engineer to anchor the offshore
seat of a small, integrated global delivery team. You will own the data and context foundation of AI enabled decision solutions — ingestion, curated and gold layers, semantic models, and platform native application surfaces — built directly on the client's enterprise platform, and you will use AI
coding agents to deliver at a pace and quality bar conventional teams do not reach. The role
combines hands-on platform engineering, system design for LLM-inclusive architectures, and the
techno-functional fluency to carry business stakeholders through technical trade-offs.
Responsibilities:
Data Architecture & Migration
- Design and execute data migration into the client's enterprise platform — Databricks, Snowflake, or the relevant hyperscaler stack — from legacy warehouses and source systems, with reconciliation and cutover discipline.
- Implement medallion architectures (bronze / silver / gold) with governed promotion between layers, data-quality gates at each stage, and lineage from ingestion through consumption.
- Own data and access readiness at engagement start: profiling, data-quality sentinels, and the pipeline foundations the build depends on.
Semantic Layer, Metric Views & Data Catalogs
- Build the semantic layer over large tabular datasets and warehouses — governed metric views, KPI definitions, and business glossaries — so downstream applications and agents consume trusted definitions rather than raw tables.
- Stand up and curate data catalogs: metadata, lineage, ownership, and discoverability across the estate.
- Build the context layer for large tabular estates: column-level meaning, business rules, and interpretation context captured in platform-native constructs, so AI applications are grounded in the client's real data and the context compounds across markets and use cases rather than being rebuilt.
Ontology & Knowledge Modeling
- Act as the engagement's ontology architect: design and build ontologies and knowledge models that formalize the client's domain — entities, relationships, KPI hierarchies, and decision rules.
- Translate SOPs, domain interviews, and undocumented working knowledge into machine-usable structures — ontologies, taxonomies, and knowledge graphs — that ground retrieval and agent reasoning.
- Version and maintain these knowledge assets so they replicate to new markets and adjacent
AI-Accelerated Delivery to a Production Bar
- Multiply personal throughput with AI coding agents: frame and direct multiple parallel build tracks, then specify, review, and harden what the agents produce — the quality bar is production engineering, not prototype output.
- Build production-ready from day one at the scope the engagement allows: CI/CD, versioned prompts and evals, secrets hygiene, and observability from the first sprint.
- Stand up the evaluation harness early and publish scores on a weekly cadence; demonstrate working software to the client from a correctly configured environment each week.
Delivery Discipline & Reuse
- Track delivery against committed timelines; make scope additions visible as priced scope trades rather than silent absorption.
- Run the engagement's quality gates and checklists through to sign-off; complete handover documentation — runbooks, ADR logs, operational artifacts — executable by client teams independently.
- Return reusable assets, learnings, and failure-register entries to the central library at closure, so each engagement hardens the next.
Required qualifications & experience
- 8+ years' experience in data engineering, data platforms, and analytics, with 10+ years of overall consulting experience.
- Completed Data Engineering Professional certification and required classes.
- Professional-level certification on at least one MathCo-preferred platform — Google Cloud (including Gemini), Databricks, AWS, Azure, or Snowflake; professional tier preferred over foundational / associate.
- Minimum 6–8+ projects delivered with hands-on development experience on Databricks or other MathCo-preferred platforms
- Working knowledge of two or more common cloud ecosystems (AWS, Azure, GCP), with deep expertise in at least one.
- Deep experience with distributed computing on Spark, including knowledge of Spark runtime internals.
- Familiarity with CI/CD for production deployments.
- Working knowledge of MLOps.
- Current knowledge across the breadth of Databricks product and platform features.
- Familiarity with optimizations for performance and scalability.
- Demonstrated experience designing complex software systems, including current experience designing systems that incorporate LLMs — as distinct from building solely with managed toolkits such as Vertex AI or Azure AI Foundry.
- Domain exposure in one or more of CPG, Retail, Pharma, or Manufacturing preferred.
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