Data Analyst

About The Role:

Incanus Technologies is looking for a Data Analyst with strong analytical thinking and hands-on experience in SQL, Python, data visualization, and data analysis for its clients.

The role involves working closely with Business, Product, Operations, Finance, and Engineering teams to analyze datasets, identify trends and patterns, generate meaningful insights, and support data-driven decision-making.

You will work with datasets from multiple sources, write SQL queries, perform analysis using Python, build dashboards and reports, track key performance metrics, investigate data anomalies, and ensure data accuracy and consistency.

This role is ideal for someone with 1 - 4 years of relevant experience in data analytics who enjoys working with data, solving analytical problems, and translating complex datasets into meaningful insights.

Important: This opportunity involves a face-to-face interview process. Candidates should be available to attend the interview in person.

Key Responsibilities:

Data Analysis & Insights:

- Analyze large and complex datasets to identify trends, patterns, anomalies, and meaningful insights.

- Perform exploratory data analysis (EDA) to understand data distributions, relationships, and key drivers.

- Clean, transform, validate, and analyze data from multiple sources.

- Conduct root-cause analysis to investigate changes in business metrics and performance.

- Perform descriptive and diagnostic analysis to understand historical trends and outcomes.

- Develop structured analyses to answer data-related questions and support decision-making.

- Identify data inconsistencies, outliers, and quality issues.

- Track key performance indicators (KPIs) and analyze changes in performance.

SQL & Database Management:

- Write and optimize SQL queries using joins, subqueries, CTEs, aggregations, window functions, and other data-manipulation techniques.

- Extract and analyze data from relational databases and analytical systems.

- Work with structured datasets across multiple tables and data sources.

- Perform data validation, reconciliation, and quality checks to ensure accuracy and consistency.

- Understand database schemas, table relationships, and data structures.

- Identify and troubleshoot data-quality issues.

- Create reusable queries and datasets for recurring analytical requirements.

Python & Data Processing:

- Use Python for data cleaning, manipulation, exploratory analysis, and automation.

- Work with Python libraries such as Pandas, NumPy, and Matplotlib.

- Write scripts to process, transform, analyze, and validate datasets.

- Perform statistical analysis and data exploration using Python.

- Automate repetitive data-processing, validation, and reporting tasks.

- Handle missing values, duplicates, outliers, and inconsistencies in datasets.

- Apply Python to solve practical data-analysis problems.

Data Visualization & Reporting:

- Build and maintain dashboards and reports to monitor business performance and key KPIs.

- Use visualization tools such as Power BI, Tableau, or Looker to present analytical findings.

- Create charts, graphs, and visualizations to communicate trends, comparisons, and patterns.

- Develop recurring reports and automated reporting solutions.

- Ensure accuracy, consistency, and reliability of dashboards and reports.

- Translate complex analytical findings into clear and understandable visual representations.

- Maintain reporting documentation and metric definitions.

Data Quality & Cross-functional Collaboration:

- Collaborate with Product, Engineering, Operations, Finance, and Business teams to understand data requirements.

- Gather analytical requirements and translate them into data queries, reports, and dashboards.

- Work with data and engineering teams to understand data sources, pipelines, and structures.

- Identify and resolve data discrepancies and reporting inconsistencies.

- Communicate analytical findings clearly to technical and non-technical stakeholders.

- Support teams with data extraction, validation, analysis, and reporting requirements.

- Ensure data accuracy and consistency across analytical outputs.

Required Skills:

- 1 - 4 years of relevant experience in Data Analytics, Business Intelligence, Reporting Analytics, Product Analytics, or a similar data-focused role.

- Strong SQL skills, including joins, subqueries, aggregations, CTEs, window functions, and data manipulation.

- Good working knowledge of Python for data cleaning, manipulation, and analysis.

- Hands-on experience working with datasets and performing exploratory data analysis.

- Strong analytical, logical reasoning, and problem-solving skills.

- Good understanding of statistics and quantitative analysis.

- Ability to clean, transform, validate, and interpret datasets.

- Understanding of data visualization, dashboarding, and reporting.

- Familiarity with relational databases and structured data.

- Ability to identify data-quality issues and perform validation checks.

- Strong numerical reasoning and attention to detail.

- Ability to communicate analytical findings clearly and effectively.

Preferred Qualifications:

- Hands-on experience with BI tools such as Power BI, Tableau, or Looker.

- Experience with Python libraries such as Pandas, NumPy, Matplotlib, or Seaborn.

- Understanding of databases, data models, ETL/ELT concepts, and data pipelines.

- Strong working knowledge of Excel or Google Sheets, including advanced formulas, pivot tables, and data analysis.

- Exposure to statistical analysis, hypothesis testing, forecasting, or analytical modeling.

- Experience working with large datasets and multiple data sources.

- Familiarity with APIs, cloud data platforms, or modern data tools.

- Understanding of query optimization and efficient data processing.

- Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.

Ideal Candidate:

We are looking for someone who:

- Has strong hands-on experience with SQL and is comfortable writing complex queries independently.

- Can use Python to clean, manipulate, analyze, and explore datasets.

- Enjoys working with data and identifying meaningful patterns and insights.

- Is curious about understanding why a metric changed, not just what changed.

- Can independently extract, clean, validate, analyze, and present data.

- Has strong numerical reasoning, logical thinking, and attention to detail.

- Understands the importance of data accuracy, consistency, and reliability.

- Can build dashboards and reports that communicate analytical findings effectively.

- Is comfortable working with large datasets and solving complex data-related problems.

- Can translate analytical findings into clear, concise, and actionable insights.

- Is comfortable learning new tools, databases, and analytical techniques quickly.

- Can independently own analytical tasks from data extraction through analysis and final reporting.

- Enjoys collaborating with technical and business teams to solve real-world data problems.