Senior Database Engineer

Havas Group; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Havas Creative in the United States seeks a Senior Database Engineer to design, build, and optimize modern Microsoft data platforms across Azure and Microsoft Fabric, owning enterprise warehouse and lakehouse architectures with AI-ready data products. The role requires 7+ years in data engineering or warehousing and hands-on experience with Copilot, LLMs, and responsible AI controls.

The architect will craft scalable lakehouse/warehouse solutions, dimensional models, ETL/ELT pipelines, and

Qualifications

• Minimum 7 years of professional data engineering, database engineering, data warehousing, or closely related experience.
• 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.

Responsibilities

• Design, build, and support enterprise data solutions using Azure and Microsoft Fabric.
• Create 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 when appropriate.
• 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 tools such as Copilot in Microsoft Fabric, GitHub Copilot, Microsoft 365 Copilot, Copilot Studio, or approved 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 throughout the engineering lifecycle.
• Use Git, pull requests, automated tests, CI/CD, environment promotion, and Infrastructure as Code for data pipelines, notebooks, database objects, and platform configuration.
• 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.

Job description

Agency :

Havas Creative

Job Description :

The Senior Database Engineer is a hands‑on senior engineering role responsible for designing, building, optimizing, and evolving modern Microsoft data platforms across Azure and Microsoft Fabric. The position owns cloud data engineering, enterprise warehouse and lakehouse architecture, data integration, semantic consumption, and AI‑ready data products. The successful candidate brings a minimum of seven years of data engineering, database engineering, or data warehousing experience, including practical experience enabling generative AI solutions with copilots, large language models, retrieval-augmented generation, embeddings, vector or hybrid retrieval, prompt engineering, model evaluation, and responsible AI controls.

• Design, build, and support enterprise data solutions using Azure and Microsoft Fabric.

• Create 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 when appropriate.

• 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 tools such as Copilot in Microsoft Fabric, GitHub Copilot, Microsoft 365 Copilot, Copilot Studio, or approved 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 throughout the engineering lifecycle.

• Use Git, pull requests, automated tests, CI/CD, environment promotion, and Infrastructure as Code for data pipelines, notebooks, database objects, and platform configuration.

• 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.

Required Qualification:

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.

General Competencies

• Hands‑on ownership, sound judgment, attention to detail, and disciplined follow‑through.
• 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.

Contract Type :

Permanent

Here at Havas across the group we pride ourselves on being committed to offering equal opportunities to all potential employees and have zero tolerance for discrimination. We are an equal opportunity employer and welcome applicants irrespective of age, sex, race, ethnicity, disability and other factors that have no bearing on an individual’s ability to perform their job.