Jobs at Nasstar

Lead end-to-end delivery of a multi-workstream, francophone data programme: run governance, manage RAID and change control, oversee resourcing and T&M commercials, coordinate cross-border squads, ensure quality gates, and author bilingual client-facing documentation.

Lead client-facing data platform architecture and strategy. Design scalable Databricks/Azure or AWS-based solutions, oversee multi-team delivery, incident response, governance, and data security. Mentor engineers, drive roadmap and budget decisions, and ensure resilient, high-quality data pipelines aligned to business goals.

Entry-level data engineer to follow a structured 12-month development programme. Build internal reporting, reusable engineering frameworks, documentation, and tooling while learning SQL, Python, Databricks and cloud data services. Support senior engineers, join code reviews, shadow customer workshops, and progressively contribute to client projects.

Design, build and maintain cloud-native ETL/ELT pipelines and data lake solutions using dbt, Snowflake/Databricks and AWS services. Optimize SQL and data models for performance and cost, create dashboards, follow CI/CD and software engineering best practices, and collaborate with architects, engineers and clients to deliver production-ready enterprise data solutions.

Own Databricks-centric presales: lead discovery, design Lakehouse reference architectures, run demos/POCs, produce RFPs/SOWs, and align Sales, Delivery, and Databricks partner teams to win data engineering, MLOps, and GenAI deals.

Drive new logo acquisition (80%) and expand existing Nasstar accounts (20%) by leading consultative Microsoft Data & AI solution sales, collaborating with Microsoft sellers, managing end-to-end sales cycles, delivering presentations/demos, and executing go-to-market and account growth strategies.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Design, build, and maintain scalable cloud-based data pipelines and ETL processes on Azure (Databricks, ADF, Synapse, ADLS). Implement data models, quality checks, governance, and performance optimization. Collaborate with architects, cloud engineers, data scientists, and stakeholders to consolidate legacy services and enable data-driven insights.

Client-facing role designing, building, and maintaining scalable Azure cloud infrastructure and data platforms. Lead IaC implementations, CI/CD automation, container orchestration, security and governance, managed services, and mentor teams to deliver reliable cloud solutions.

Lead design and implementation of data governance and MDM strategies, develop policies and metadata/lineage practices, enforce data quality, deploy governance tooling, engage stakeholders, ensure regulatory compliance, and mentor team members.

Lead design and delivery of cloud-native, serverless web and mobile applications using React/React Native, Node.js and TypeScript. Own IaC with Terraform, implement AWS Lambda solutions, build CI/CD pipelines, apply containerization (Docker/Kubernetes/ECS), use AI-assisted tooling, mentor junior engineers, and present architectures to client stakeholders.

Design, build, and deploy AI systems (predictive models, NLP, generative AI) from PoC to production. Work with clients to define use cases, prepare large datasets, implement deployment and monitoring pipelines using cloud-native tools, and collaborate with cross-functional teams to operationalize AI solutions.

Client-facing cloud engineer designing, building, and operating scalable AWS infrastructure and data-platform foundations. Lead IaC implementations, CI/CD automation, container orchestration, security and governance, managed services, technical presales, and mentor junior engineers.

Design, build, and maintain scalable cloud-based data platform and ETL pipelines on Azure. Develop data models, ensure data quality, optimize performance, implement governance and security, and collaborate with stakeholders and data scientists to enable data-driven solutions.