Job Description

- About the team

QAD | Redzone is building an enterprise AI platform to support AI-enabled and agentic workflows across the
organisation. The AI Centre of Excellence (CoE) is accountable for turning that ambition into deployed,
measured, production workflows across priority business functions.

The CoE is small, senior and high-leverage. AI Engagement Specialists own the demand side with the
functions; the AI Architect and Senior AI Engineer build the platform and the agents; AI Operations owns the
run side. The Senior Data Engineer & Architect owns what every agent depends on: getting the right data to agents, current, governed and in a shape they can use.

- Why this role exists

Every AI use case in the CoE portfolio depends on data: from sales and customer systems, from support and
engineering knowledge sources, and from delivery and operational systems. Before an agent can use it, that
data has to be joined, current, governed and checked against what each user is allowed to see.
This role makes data a shared, reusable capability of the AI CoE rather than something rebuilt for each use
case. It owns the data layer at feasibility and development) stages and keeps the data running after go-live.
Role overview

The AI CoE Senior Data Engineer & Architect owns the data layer of the AI lifecycle. You will assess data
feasibility for each new use case, build and run the pipelines that feed the AI platform, model the semantic
layer that agents query instead of raw tables, and make sure every agent sees only the data its user is
allowed to see.This is a hands-on engineering role with production ownership, measured on agents getting correct, current and permitted data, and on each new use case reaching data-ready faster than the last.

- Where this role sits





Reports to Head of the AI Centre of Excellence
Works with Senior AI Engineer daily; AI Architect on reference architecture; AI
Engagement Specialist on data feasibility; AI Ops Lead on pipeline health
and cost; system integrators; data owners in each function; Security and
GRC

Owns Data feasibility assessments, AI data pipelines and connectors, semantic
models and metrics for AI use, retrieval-ready knowledge sources, data
access patterns for agents, and data freshness and quality targets

QAD | Redzone — AI CoE Data Engineer · AI Centre of Excellence

Accountable for Correct, current, governed data for every agent in production, measured
on freshness, quality, reuse of semantic models and time from PRD
approval to data-ready

Location India

- What you will own

1) Data feasibility and readiness

• Assess the data behind every PRD before build is approved: sources, owners, volumes, quality, access
paths and gaps, written up as a data readiness view the stage gate can act on, with what is missing, stale
or not permitted and options to fix it.
Pipelines and integration
• Build and run the pipelines that feed the AI platform (on AWS today) and the warehouses agents query,
such as BigQuery or Redshift, with incremental and, where it matters, event-driven sync.
• Close freshness gaps: know how long a new record or document takes to reach an agent,



set a target
with the business, and meet it. Handle deletions and permission changes, not just new records.
• Build and maintain connectors with the system integrators, so each new source is added once and
reused across use cases.
Semantic layer
• Model the business entities and metrics agents need in the semantic layer, so agents query governed
definitions agreed with data owners, consistent with what dashboards report, instead of writing SQL
against raw tables.
• Put guardrails on structured queries: read-only access, row limits, timeouts and cost limits for any agent
that queries data.

2) Retrieval-ready knowledge

• Prepare unstructured sources for retrieval with the Senior AI Engineer: metadata, release and version
tags, access tags, and structure that keeps the source context intact; build and maintain the golden
datasets used to test retrieval and answer quality.
Data access, security and governance
• Make agents act as the user: user-scoped access, row and column-level security in the semantic layer,
and no over-privileged shared service accounts.
• Handle sensitive data properly with Security and GRC: classification, masking of personal and customer
data in tickets, residency rules for regulated regions, lineage and audit evidence.
Run and improve (L5)
• Own pipeline health in production: freshness and quality monitoring, alerts someone acts on, and rapid
recovery when a source system changes its schema; with AI Ops, keep processing, storage and
embedding spend per use case in check.
• Turn each build into reusable data products so the next use case starts from shared models and
connectors, not from scratch.

📌 AI CoE Senior Data Engineer & Architect (Pune)
🏢 QAD
📍 Pune