Junior Data Engineer

Hyderabad · Posted 2h ago

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At a glance

Location: Hyderabad, India
Work type: Onsite
Category: Data

Junior Data Engineer at Crowe in Hyderabad, India. Role focuses on designing, developing, and maintaining data solutions using Microsoft stack (Azure Data Factory, Synapse, Databricks, Power BI) with 0–2 years experience. Responsibilities include building data pipelines, data ingestion/transformation, SQL development, data quality and governance, and collaboration with analysts and stakeholders.

What they are looking for

SqlAzure data factoryAzure synapse analyticsAzure databricksPower bi

Location: Hyderabad Telangana India

Your Journey at Crowe Starts Here

At Crowe, you can build a meaningful and rewarding career. With real flexibility to balance work with life moments, you’re trusted to deliver results and make an impact. We embrace you for who you are, care for your well-being, and nurture your career. Everyone has equitable access to opportunities for career growth and leadership. Over our 80-year history, delivering excellent service through innovation has been a core part of our DNA across our audit, tax, and consulting groups. That’s why we continuously invest in innovative ideas, such as AI-enabled insights and technology-powered solutions, to enhance our services. Join us at Crowe and embark on a career where you can help shape the future of our industry.

Job Description

Job Description: Data Engineer – Microsoft Stack

Experience Level: 0–2 years

Title: Junior Data Engineer

Role Summary

We are looking for an enthusiastic and detail-oriented Data Engineer with 0–2 years of experience to support the design, development, and maintenance of data solutions using the Microsoft technology stack. The candidate will work with data platforms, pipelines, databases, and analytics teams to help transform raw data into reliable, secure, and business-ready datasets.

Key Responsibilities

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.

Required Technical Skills

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.

Exposure to Python, PySpark, or scripting for data processing is preferred.

Basic understanding of Power BI, semantic models, dashboards, and reporting concepts is an added advantage.

Awareness of data security, role-based access control, encryption, data privacy, and governance concepts.

Familiarity with Git, Azure DevOps, CI/CD concepts, or Agile delivery practices is preferred.

Qualification Requirements

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 if they have completed 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.

Good to have Certification Requirements

The candidate must have at least one relevant Microsoft certification or Training completed before joining or within the hiring eligibility window defined by the organization.

Microsoft Certified: Azure Data Fundamentals (DP-900) or Data Engineer related training certification.

Preferred for consideration: Microsoft Certified: Fabric Analytics Engineer Associate (DP-600), Microsoft Certified: Fabric Data Engineer Associate, or equivalent Microsoft data engineering certification aligned to current Microsoft certification paths.

Additional advantage: 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.

Preferred Attributes

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.