Forward-Deployed Data & Context Engineer

MathCo; Bengaluru; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

MathCo in Bengaluru seeks a seasoned forward-deployed data and context engineer to anchor offshore delivery. You will own the data and context foundation of AI-enabled decision solutions — ingestion, curated and gold layers, semantic models, and platform native surfaces built on the client's enterprise platform, and you will use AI coding agents to deliver at pace.

The role combines hands-on platform engineering, system design for LLM-inclusive architectures, and the techno-functional fluency to

Qualifications

• 8+ years' experience in data engineering, data platforms, and analytics.
• 10+ years of overall consulting experience.
• Certification on at least one MathCo-preferred platform (e.g., Google Cloud, Databricks, AWS, Azure, Snowflake).
• Minimum 6–8+ projects delivered with Databricks or similar platforms.
• Working knowledge of two or more cloud ecosystems (AWS, Azure, GCP).
• Deep experience with Spark and CI/CD for production deployments.
• Knowledge of MLOps and platform-native features for AI-enabled delivery.

Responsibilities

• Design and execute data migration into the client's enterprise platform from legacy warehouses and source systems.
• Implement medallion architectures (bronze/silver/gold) with data quality gates and lineage.
• Own data readiness, profiling, and data-quality sentinels for engagements.

Skills

Data engineering

Data platforms

Analytics

CI/CD

MLOps

Spark

Ontology design

Education

Data Engineering Professional certification

Tools

Databricks

Snowflake

Google Cloud

AWS

Azure

Snowflake

Job description

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:

• 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.
• 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.