Sr Data Ops Developer

India·Bangaloresenior

OtherDeveloper

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Key Responsibilities

Perform all duties and maintain all standards in accordance with company policies, procedures, and internal controls such as SOX. Design, implement,

Technical Tools

OtherDeveloper

~2 min read

→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.

→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.

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.