AI CoE Senior Data Engineer & Architect

QAD; Pune District; Ibrido; INR 3.000.000 - 6.000.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health and well-being programs

Growth and learning opportunities

Collaborative culture

Descrizione del lavoro

QAD | Redzone is seeking a Data Engineer for the AI Centre of Excellence in Pune. You will own data feasibility, build pipelines feeding the AI platform on AWS, and maintain a semantic layer and retrieval-ready knowledge for agents.

Collaborate with AI engineers, data owners, and system integrators to ensure data freshness, quality, and governance while managing cost and performance in a production environment.

Competenze

• 8+ years in data engineering, including at least 3 years building data for ML/Gen AI in production.
• Strong SQL and Python, and pipeline orchestration using Airflow, dbt, AWS Glue or similar.
• Cloud data platforms: AWS, Azure, Google BigQuery.
• Experience with data retrieval, semantic models, and data quality targets.
• Excellent collaboration with data owners and cross-functional teams.

Mansioni

• Own data feasibility assessments and data pipelines for the AI platform.
• Build and maintain semantic layer and retrieval-ready knowledge.
• Ensure data security, access controls, and governance.
• Collaborate with AI engineers, data owners, and system integrators.
• Monitor pipeline health and cost in production.

Conoscenze

8+ years of data engineering

Strong SQL & Python

Strumenti

Airflow

dbt

AWS Glue

AWS (S3, Redshift)

Azure

Google BigQuery

LookML / semantic modeling

Descrizione del lavoro

QADis a global leader in cloud-based enterprise solutions, empowering businesses to adapt faster and thrive in an increasingly dynamic, digital world. With a strong focus on innovation and AI-driven insights, QAD is shaping the future of enterprise transformation across industries. We foster a flexible, inclusive culture where bold ideas and future-focused talent to drive real impact. Join us to help build the intelligent, connected enterprise of tomorrow.

This hybrid position requires candidates to be based in Pune, with 3-4 days of in-office collaboration per week.

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

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 fast 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.

Qualifications

• 8+ years in data engineering, including at least 3 years building data for ML, analytics or Gen AI products
  running in production.
• Strong SQL and Python, and pipeline orchestration in practice, for example Airflow, dbt, AWS Glue or similar.
• Cloud data platforms: Azure , AWS (S3, Glue, Redshift, RDS) and/or Google BigQuery.
• Semantic and dimensional modelling: hands-on with a semantic or metrics layer such as Cube, dbt Semantic Layer, LookML or Power BI semantic models.
• Incremental and event-driven ingestion: change data capture, webhooks or streaming, and handling deletes and late-arriving data.
• Data for retrieval: document ingestion, metadata design and hybrid or vector search (for example OpenSearch, Elasticsearch or pgvector), and enough understanding of embeddings to work with an AI engineer.
• Data security in practice: role-based access, row and column-level security, handling personal data, and cloud identity and access management.
• API integration with enterprise SaaS, such as CRM and Atlassian REST APIs.
• Data quality discipline: tests, freshness and quality monitoring, and service-level targets for data.
• Clear communication with data owners and business teams: able to explain what the data can and cannot support, and why.

We are looking for someone who has run pipelines in production and lived with the consequences, and can tell when an agent's wrong answer is really a data problem.

Strongly preferred

• Sales and customer data, support and engineering knowledge data, or ERP and manufacturing data;
  Progress OpenEdge familiarity is a plus.
• Experience with an enterprise RAG or AI platform, and with exposing data to agents through APIs or MCP tools.
• Working alongside system integrators and a central data team, and across time zones; enterprise B2B SaaS experience.

What this role is not

• Not a BI developer. Dashboards are owned by the functions and AI Ops; you make the data behind them and the agents trustworthy.
• Not the owner of the enterprise data platform. You work with the central data team and reuse their assets, and build what AI use cases need on top.
• Not an AI engineer. Agent logic, prompts and models sit with the Senior AI Engineer; you own the data they depend on.
• Not a one-off integration contractor. You own the pipelines and semantic models in production, after go-live.

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

What success looks like

First 3 months

• Data feasibility assessments completed for the priority use cases.
• A data readiness checklist built into the stage gates and used on every new use case.
• An agreed access pattern with Security for user-scoped data, and freshness measured for priority data sources.
• Semantic layer version 1 live for the priority domain, used by production agents.
• Freshness and quality targets set and monitored for every production data source.

Additional Information

• Your health and well being are important to us at QAD. We provide programs that help you strike a healthy work-life balance.
• Opportunity to join a growing business, launching into its next phase of expansion and transformation.
• Collaborative culture of smart and hard-working people who support one another to get the job done.
• An atmosphere of growth and opportunity, where idea-sharing is always prioritized over level or hierarchy.
• Compensation packages based on experience and desired skill set

About QAD:

QAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD | Redzone helps manufacturers operate with Champion Pace , achieving measurable productivity, resilience, and growth in just 90 days.

QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.