Data Analytics & Reporting Automation Engineer

We are seeking a skilled Data Analytics & Reporting Automation Engineer with strong expertise in SQL, Python, Business Intelligence (BI), and reporting automation.The ideal candidate will be responsible for transforming complex datasets into meaningful business insights, developing automated reporting solutions, and building interactive dashboards to support data-driven decision-making.This role requires hands-on experience in data extraction, transformation, analysis, visualization, and workflow automation, along with strong analytical and problem-solving abilities.

Key Responsibilities

1. Data Extraction & Management

• Develop, optimize, and maintain complex SQL queries for data extraction and analysis.
• Design and manage ETL/ELT workflows to integrate data from multiple sources.
• Perform data cleaning, transformation, validation, and aggregation.
• Develop reusable datasets, database views, stored procedures, and analytical data models.
• Identify and resolve data quality issues while improving query performance.

2. Reporting & Workflow Automation

• Design and automate daily, weekly, and monthly business reports.
• Develop scheduled workflows for automated data processing, report generation, and distribution.
• Integrate databases, APIs, spreadsheets, and reporting platforms.
• Implement automated data validation, reconciliation, and exception monitoring.
• Troubleshoot reporting failures and ensure timely, accurate delivery.

3. Data Analytics & Business Intelligence

• Analyze business performance, operational KPIs, and historical trends.
• Conduct descriptive and diagnostic analysis to identify patterns, anomalies, and performance gaps.
• Apply statistical analysis and forecasting techniques for predictive insights.
• Explore prescriptive analytics to recommend improvements and support business decisions.
• Translate business requirements into measurable KPIs and actionable insights.

4. Dashboard Development & Visualization

• Design and maintain interactive dashboards using Power BI, Tableau, Looker, Metabase, or similar BI tools.
• Present complex information through intuitive charts, visualizations, and performance reports.
• Ensure dashboards are accurate, efficient, scalable, and user-friendly.
• Communicate analytical findings and business recommendations to stakeholders.

5. Data Quality & Process Improvement

• Establish data validation, reconciliation, and reporting quality standards.
• Document SQL queries, reporting logic, data definitions, and automation workflows.
• Identify opportunities to reduce manual reporting and improve operational efficiency.
• Collaborate with product, engineering, operations, and business teams to deliver reliable analytics solutions.

Required Skills & Qualifications

• Bachelor’s degree in Computer Science, IT, Data Science, Statistics, or a related discipline.
• 3-5 years of relevant experience in Data Analytics, BI, Reporting Automation, Analytics Engineering, or Data Engineering.
• Advanced SQL proficiency, including joins, CTEs, window functions, subqueries, aggregations, and query optimization.
• Hands-on experience with MySQL, PostgreSQL, SQL Server, or similar relational databases.
• Strong Python skills for data processing, analysis, and automation.
• Practical experience with at least one BI platform such as Power BI, Tableau, Looker, or Metabase.
• Experience developing automated reports and recurring data workflows.
• Strong understanding of data modeling, ETL/ELT processes, data transformation, and validation.
• Knowledge of descriptive, diagnostic, predictive, and prescriptive analytics.
• Excellent analytical, problem-solving, communication, and stakeholder management skills.

Preferred Skills

• Experience with workflow orchestration tools, scheduled jobs, and automated report distribution.
• Familiarity with REST APIs and multi-source data integration.
• Knowledge of statistical modeling, regression, forecasting, and basic machine learning.
• Understanding of data warehousing and dimensional modeling.
• Exposure to AWS, Azure, or Google Cloud data services.
• Familiarity with Git, testing, monitoring, logging, and deployment practices.
• Experience using AI-assisted tools for analytics and reporting automation.