Description

What you will be doing:

• Perform all duties and maintain all standards in accordance with company policies, procedures, and internal controls such as SOX.
• Design, implement, and maintain cloud-based data pipelines to enable efficient storage, processing, and analysis of large volumes of data.
• Collaborate with data engineers, and other stakeholders to understand requirements and translate them into scalable and reliable data platform architectures.
• Evaluate and select appropriate cloud infrastructure and services to support data storage, processing, and analytics needs, considering factors such as scalability, performance, extendibility, cost, and security.
• Implement data ingestion pipelines, integrating various, disparate data sources and ensuring data quality, integrity, and timeliness.
• Design and optimize data storage solutions, including data lakes, data warehouses, and data marts, to support different types of data processing and analysis.
• Implement data processing and transformation workflows using technologies such as Azure Spark, Azure SQL Pools / data warehouses or other cloud-native data processing services.

What you will likely bring:

• 3+ years’ experience in data engineering, analytics engineering, DataOps, business intelligence engineering, or a related technical field.
• Strong SQL skills and experience working with data pipelines, data platforms, data warehouses, data lakes, or similar data environments.
• Experience gathering requirements from business stakeholders and explaining technical concepts clearly to technical and nontechnical audiences.
• Strong analytical, strategic-thinking, and creative problem-solving skills, with curiosity to identify trends, investigate details, and improve processes.
• Excellent written and verbal communication skills, with the ability to synthesize significant amounts of information and tailor it to the intended audience.

What could set you apart:

• Cloud & DevOps: Hands-on Azure development beyond data pipelines, including infrastructure-as-code (e.g., Bicep, Terraform, or ARM) and DevOps practices such as CI/CD, Git-based source control workflows, and automated deployment of data assets.
• AI-assisted development: Practical experience using AI coding assistants (e.g., ChatGPT, GitHub Copilot/Codex) to accelerate development, testing, and code review within a disciplined, version-controlled workflow.
• Applied AI / GenAI: Exposure to building AI-driven solutions — retrieval-augmented generation (RAG), agentic workflows, vector databases, and orchestration frameworks such as LangChain or LangGraph.
• Lightweight application development: Ability to stand up simple internal applications or APIs (e.g., Python, FastAPI) to surface data and tooling to end users.
   

Key Skills

Education

Any Graduate