AI CoE Senior Data Engineer & Architect

AI CoE Senior Data Engineer & ArchitectFull-timeDepartment: Information TechnologyLocation: India - Pune - Adjunct 0fficeCompany DescriptionQAD is 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 DescriptionAbout the teamQAD | Redzone is building an enterprise AI platform to support AI-enabled and agentic workflows across theorganisation. 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 thefunctions; the AI Architect and Senior AI Engineer build the platform and the agents; AI Operations owns therun 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 existsEvery AI use case in the CoE portfolio depends on data: from sales and customer systems, from support andengineering knowledge sources, and from delivery and operational systems. Before an agent can use it, thatdata 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 usecase. It owns the data layer at feasibility and development) stages and keeps the data running after go-live.Role overviewThe AI CoE Senior Data Engineer & Architect owns the data layer of the AI lifecycle. You will assess datafeasibility for each new use case, build and run the pipelines that feed the AI platform, model the semanticlayer that agents query instead of raw tables, and make sure every agent sees only the data its user isallowed 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 sitsReports to Head of the AI Centre of ExcellenceWorks with Senior AI Engineer daily; AI Architect on reference architecture; AIEngagement Specialist on data feasibility; AI Ops Lead on pipeline healthand cost; system integrators; data owners in each function; Security andGRCOwns Data feasibility assessments, AI data pipelines and connectors, semanticmodels and metrics for AI use, retrieval-ready knowledge sources, dataaccess patterns for agents, and data freshness and quality targetsQAD | Redzone — AI CoE Data Engineer · AI Centre of ExcellenceAccountable for Correct, current, governed data for every agent in production, measuredon freshness, quality, reuse of semantic models and time from PRDapproval to data-readyLocation IndiaWhat you will own1) Data feasibility and readiness• Assess the data behind every PRD before build is approved: sources, owners, volumes, quality, accesspaths and gaps, written up as a data readiness view the stage gate can act on, with what is missing, staleor 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 targetwith 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 andreused across use cases.Semantic layer• Model the business entities and metrics agents need in the semantic layer, so agents query governeddefinitions agreed with data owners, consistent with what dashboards report, instead of writing SQLagainst raw tables.• Put guardrails on structured queries: read-only access, row limits, timeouts and cost limits for any agentthat queries data.2) Retrieval-ready knowledge• Prepare unstructured sources for retrieval with the Senior AI Engineer: metadata, release and versiontags, access tags, and structure that keeps the source context intact; build and maintain the goldendatasets 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 customerdata 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 fastrecovery when a source system changes its schema; with AI Ops, keep processing, storage andembedding spend per use case in check.• Turn each build into reusable data products so the next use case starts from shared models andconnectors, not from scratch.QualificationsEssential• 8+ years in data engineering, including at least 3 years building data for ML, analytics or Gen AI productsrunning in production.• Strong SQL and Python, and pipeline orchestration in practice, for example Airflow, dbt, AWS Glue orsimilar.• 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, dbtSemantic Layer, LookML or Power BI semantic models.• Incremental and event-driven ingestion: change data capture, webhooks or streaming, and handlingdeletes and late-arriving data.• Data for retrieval: document ingestion, metadata design and hybrid or vector search (for exampleOpenSearch, Elasticsearch or pgvector), and enough understanding of embeddings to work with an AIengineer.• Data security in practice: role-based access, row and column-level security, handling personal data, andcloud 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 andcannot support, and why.Data depthWe are looking for someone who has run pipelines in production and lived with the consequences, and cantell 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 orMCP tools.• Working alongside system integrators and a central data team, and across time zones; enterprise B2BSaaS experience.What this role is not• Not a BI developer. Dashboards are owned by the functions and AI Ops; you make the data behind themand the agents trustworthy.• Not the owner of the enterprise data platform. You work with the central data team and reuse theirassets, 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 datathey depend on.QAD | Redzone — AI CoE Data Engineer · AI Centre of Excellence• Not a one-off integration contractor. You own the pipelines and semantic models in production, aftergo-live.What success looks likeFirst 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 datasources.• 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 InformationYour 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 setAbout QAD:QAD|Redzoneis 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 withChampion 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.By clicking the link above or any third-party link within this posting, you are leaving this site and going to a third-party website where the third-party website's terms and privacy policy applyI'm interestedI'm interestedPrivacy Notice