Junior Data Engineer

Overview

Support the design, development, and maintenance of data solutions on the Microsoft technology stack, transforming raw data into reliable, secure, business-ready datasets.

What you'll do

• Assist in building, testing, and maintaining data pipelines using Microsoft Azure services such as Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Microsoft Fabric, or related tools.
• Support data ingestion, transformation, cleansing, and loading activities from structured, semi-structured, and unstructured data sources.
• Work with SQL Server, Azure SQL Database, Azure Data Lake Storage, and other Microsoft data platforms to store, process, and manage data.
• Write and optimize SQL queries, stored procedures, and basic data transformation scripts.
• Collaborate with data analysts, BI developers, data scientists, and business stakeholders to understand data requirements and deliver reliable datasets.
• Assist in implementing data quality checks, validation rules, monitoring, and troubleshooting of data pipelines.
• Support reporting and analytics initiatives using Power BI datasets, semantic models, or data marts as required.
• Follow data security, access control, governance, and compliance standards while handling enterprise data.
• Document data flows, pipeline logic, data mappings, and technical specifications.
• Continuously learn and apply best practices in cloud data engineering, automation, performance optimization, and modern data architecture.

What you'll need

• Basic to intermediate knowledge of SQL, relational databases, and database design concepts.
• Hands-on academic, internship, project, or work exposure to Microsoft SQL Server, Azure SQL Database, or equivalent relational database platforms.
• Understanding of ETL/ELT concepts, data pipelines, data warehousing, data lakes, and data modeling fundamentals.
• Basic knowledge of Microsoft Azure data services such as Azure Data Factory, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, or Microsoft Fabric.
• Awareness of data security, role-based access control, encryption, data privacy, and governance concepts.
• Bachelor's degree in Computer Science, Information Technology, Data Science, Software Engineering, Electronics, Mathematics, Statistics, or a related technical discipline.
• 0–2 years of relevant experience in data engineering, database development, cloud data platforms, BI engineering, or analytics engineering. Fresh graduates may be considered with relevant academic projects, internships, capstone projects, or hands-on training in Microsoft data technologies.
• Strong analytical, problem-solving, and logical reasoning skills.
• Ability to understand business requirements and convert them into technical data solutions under guidance.
• Good communication skills and ability to work collaboratively in a team environment.
• Willingness to learn Microsoft Azure, Microsoft Fabric, data engineering best practices, and modern cloud analytics platforms.
• At least one relevant Microsoft certification or training completed before joining or within the hiring eligibility window defined by the organization.

Nice to have

• Exposure to Python, PySpark, or scripting for data processing.
• Basic understanding of Power BI, semantic models, dashboards, and reporting concepts.
• Familiarity with Git, Azure DevOps, CI/CD concepts, or Agile delivery practices.
• Microsoft Certified: Azure Data Fundamentals (DP-900) or Data Engineer related training certification.
• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600), Microsoft Certified: Fabric Data Engineer Associate, or equivalent Microsoft data engineering certification.
• Power BI Data Analyst Associate certification or hands-on Microsoft Learn achievements related to Azure Data Factory, Microsoft Fabric, Azure Synapse, Azure SQL, or Power BI.
• Curiosity to learn new data technologies and apply them in real-world business scenarios.
• Attention to detail in data validation, documentation, and quality checks.
• Ability to work with large datasets and troubleshoot data-related issues.
• Strong ownership mindset and willingness to work in a fast-paced delivery environment.
• Interest in cloud data platforms, AI-ready data preparation, analytics engineering, and enterprise reporting solutions.
• Optional exposure to AI adoption practices, including awareness of how AI tools, copilots, or automation can improve data engineering productivity, documentation, testing, and analytics workflows.

Details

• Location: Hyderabad, Telangana, India.