Data Analyst – Azure Data Engineering

Hiring Location: Bengaluru
Experience Range: 6 to 8 Years
Education Qualification: B.Tech or equivalent in Computer Science, Information Technology, or related field

Role Proficiency:

Provide expertise in data analysis techniques and software tools under supervision. Streamline business processes through data-driven insights and automation. Support reporting, data modeling, and decision-making through end-to-end analytics workflows.

Key Outcomes:

Design and manage secure, scalable reporting environments including metadata and access control.

Provide technical guidance on data storage, mining, transformation, and cleansing.

Support data warehouse and BI needs including requirements revision and normalization.

Assess and implement new software or upgrades to improve data infrastructure.

Deliver data-driven insights using visualizations and written summaries.

Gather client feedback and refine analytics solutions accordingly.

Train end users on new dashboards and analytical tools.

Identify opportunities for automating or optimizing business processes.

Set FAST goals and provide ongoing feedback to team members.

Measures of Success:

Code quality and number of review comments

Data integrity and consistency across systems

Quantity and impact of data models implemented

Business process improvements driven by analytics

Number and quality of BI dashboards delivered

Timely completion of mandatory training

Feedback received from clients on analytics outputs

Outputs Expected Determine Data Needs:

Work with stakeholders to define data requirements for business analysis and reporting.

Insights Generation:

Mine and analyze data for actionable insights and trends.

Data & Code Development:

Write reusable and optimized SQL and Python (or PySpark) code.

Create and validate data models for predictive and prescriptive analytics.

Implement source-controlled solutions using tools like Git or Bitbucket.

BI & Reporting:

Build reports and dashboards using Power BI for business visualization.

Illustrate findings through clear visuals and translate into business language.

Automation & Integration:

Automate data pipelines and tasks using PowerShell and Power Automate.

Develop ETL/ELT workflows using Azure Data Factory (ADF).

Documentation & Knowledge Management:

Document processes and models; participate in peer reviews.

Maintain project documentation and knowledge repositories.

Status & Communication:

Regularly update project status, risks, and progress reports.

Collaborate cross-functionally with data engineers, analysts, and business teams.

Must Have Skills:

Azure Data Factory (ADF) – for ETL/ELT pipelines

Databricks or Azure Synapse Analytics – large-scale data processing

SQL Server – queries, stored procedures, and views

Power BI – interactive dashboards and data visualizations

PowerShell – automation and task scripting

Power Automate – workflow and notification automation

Python/PySpark – for advanced data manipulation and orchestration

Strong understanding of data warehousing, data modeling, and performance tuning

Excellent analytical, documentation, and communication skills

Good to Have Skills:

Experience with:

Azure Data Lake Storage (ADLS) and Delta Lake

DevOps, CI/CD pipelines, and version control tools (Git)

Data governance, security, and compliance frameworks (e.g., GDPR, HIPAA)

Familiarity with:

Agile methodologies, Scrum/Kanban practices

Tools like SAS, Tableau, MATLAB, Qlik

Skill Examples:

Analyze and visualize large datasets for business decisions.

Communicate analytical findings across technical and non-technical teams.

Develop predictive and prescriptive models using modern data tools.

Facilitate meetings and presentations with stakeholders.

Collaborate in team environments and offer proactive support.

Maintain accuracy and attention to detail in data interpretation.

Work efficiently under deadlines across multiple data tasks.

Knowledge Examples:

SQL, Python, and data modeling languages

BI tools (Power BI, Tableau, Qlik)

Data platforms (Azure Synapse, ADLS, SQL Server)

Version control tools (Git, Bitbucket)

Agile tools and project tracking (e.g., Jira)

Spreadsheet tools (Excel, Google Sheets)

Domain understanding for customized data solutions