Senior Database Engineer

Job not on LinkedIn

🕒 September 2

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🔙 Backend Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Havas

WebsiteLinkedInAll Job Openings

10,000+ employees

📣 Marketing

📱 Media

💼 Consulting

Marketing • Media • Consulting

Havas is a global advertising and communications network that provides integrated marketing, creative, media, PR and consultancy services. The company offers Business Consultancy & Transformation, Brand Consultancy & Design, Creative, Media, Health & Wellness communications, Customer Experience, PR and Public Affairs, Brand Partnerships, Sponsorship & Events, and large-scale Production & Content services. Havas emphasizes data- and AI-driven work (Converged. AI), sustainability and purpose-driven initiatives such as Meaningful Brands and Prosumer Reports, and serves clients with creative campaigns and corporate communications worldwide.

📋 Description

• Design, build, optimize, and evolve modern Microsoft data platforms across Azure and Microsoft Fabric • Own cloud data engineering, enterprise warehouse and lakehouse architecture, data integration, semantic consumption, and AI-ready data products • Design scalable Fabric Lakehouse, Fabric Warehouse, and hybrid warehouse architectures with raw, standardized, curated, and consumption-ready layers • Design dimensional models, star schemas, facts, dimensions, data marts, semantic-ready datasets, and reusable data products • Build production ETL/ELT using Fabric Data Factory, Azure Data Factory, SQL, Python, PySpark, notebooks, APIs, files, and event or batch integration patterns • Engineer OneLake-aligned solutions, shortcuts, pipelines, notebooks, SQL endpoints, Power BI semantic models, and Direct Lake patterns • Create governed data foundations for generative AI, copilots, agents, search, and analytics using Microsoft Foundry or Azure OpenAI, Azure AI Search, and enterprise data sources • Design and implement RAG workflows including ingestion, chunking, metadata, embeddings, vector/hybrid retrieval, grounding, prompt design, citations, and evaluation • Evaluate LLM solution quality, grounding, latency, cost, content safety, data leakage risk, and business fitness before production use • Use enterprise copilots to accelerate development and build user-facing experiences • Apply responsible AI practices, human review, access controls, privacy protections, prompt and model testing, auditability, and monitoring • Implement data quality, lineage, observability, reconciliation, validation, security, and governance controls • Use Git, pull requests, automated tests, CI/CD, environment promotion, and Infrastructure as Code • Optimize workloads for performance, scalability, reliability, and cost across SQL, Spark, Fabric capacity, storage, pipelines, and semantic models • Partner with BI, analytics, application engineering, DBAs, security, infrastructure, and business stakeholders on end-to-end solution architecture • Provide technical leadership, code and design reviews, reusable templates, technical documentation, and mentoring

🎯 Requirements

• Minimum 7 years of professional data engineering, database engineering, data warehousing, or closely related experience, including senior-level design ownership • Hands-on production experience with Azure data services and Microsoft Fabric or equivalent Microsoft cloud lakehouse/warehouse technologies • Advanced SQL and strong knowledge of relational design, dimensional modeling, data warehousing, data marts, and performance engineering • Experience building and operating ETL/ELT pipelines with SQL, Python or PySpark, notebooks, APIs, files, orchestration, and incremental processing patterns • Practical AI/LLM experience, including at least one implemented Copilot, chatbot, agent, RAG, intelligent search, or LLM-enabled data solution • Experience with prompt engineering, grounding, embeddings, vector or hybrid retrieval, model evaluation, responsible AI, security, and observability • Experience with Git, pull requests, automated testing, CI/CD, environment promotion, and repeatable deployment practices • Strong understanding of identity, cloud security, data governance, privacy, data quality, lineage, and operational support • Clear written and verbal communication with technical and non-technical stakeholders • Ability to mentor team members, lead design or incident reviews, and document repeatable standards • Commitment to security, data privacy, responsible technology use, and continuous learning

🏖️ Benefits

• Equal opportunity employment • Permanent employment contract