Assistant Data Analytics Engineer

The Assistant Data Analytics Engineer is responsible for building scalable data solutions that enable rapid, data-driven decision-making, improve operational efficiency through automation, and ensure reliable, high-quality reporting systems. This role focuses on dashboard development, data pipeline automation, and maintaining a robust reporting ecosystem to support program performance and organizational decision-making.

The ideal candidate is highly analytical, proficient in SQL and Python, experienced in dashboard development, and passionate about leveraging data engineering and AI to solve business problems.

Key Responsibilities:

1. Data Analytics, Reporting & Decision Support

• Design, develop, and maintain interactive dashboards to monitor program performance, KPIs, and operational risks.
• Deliver dashboards and reporting solutions within defined turnaround timelines (TAT).
• Analyze quantitative and qualitative data to identify trends, anomalies, risks, and opportunities.
• Generate actionable insights that enable timely, data-driven course correction.
• Collaborate with stakeholders to continuously improve dashboards based on user feedback.
• Ensure timely availability of accurate reports for management and program teams.

2. Data Engineering & Automation

• Build, maintain, and optimize automated ETL/ELT data pipelines for efficient data processing.
• Automate repetitive data preparation, validation, and reporting tasks using SQL and Python.
• Develop reusable scripts and scalable automation frameworks to improve productivity.
• Optimize workflows to reduce manual effort and enhance data processing efficiency.
• Identify opportunities for automation and continuous process improvement.

3. Data Infrastructure & Reporting Ecosystem

• Develop and maintain a scalable, user-friendly reporting and analytics ecosystem.
• Ensure data accuracy, consistency, integrity, and validation across multiple data sources.
• Standardize KPIs, reporting templates, business rules, and data definitions.
• Support dashboard adoption through stakeholder training and technical assistance.
• Maintain comprehensive documentation for dashboards, databases, data pipelines, and reporting processes.
• Monitor and improve data quality and reporting reliability.

4. Innovation & Continuous Improvement

• Utilize AI-powered tools to enhance analytics workflows and improve productivity.
• Explore emerging technologies, automation frameworks, and modern analytics practices.
• Stay current with advancements in data engineering, analytics, AI, and visualization tools.
• Contribute to knowledge sharing, documentation, and capability building within the team.
• Recommend and implement improvements in tools, processes, and reporting methodologies.

Required Skills & Competencies:

Technical Skills

• Strong proficiency in SQL (PostgreSQL preferred).
• Strong programming skills in Python (Pandas, Polars).
• Experience developing dashboards using Looker Studio or similar BI tools (Power BI, Tableau).
• Knowledge of ETL/ELT pipeline development and workflow automation.
• Working knowledge of Linux environments.
• Experience with data cleaning, transformation, validation, and modeling.
• Understanding of relational databases and data warehouse concepts.
• Exposure to machine learning using Python is an added advantage.
• Familiarity with AI tools and modern analytics technologies.

Analytical & Soft Skills

• Strong analytical and problem-solving abilities.
• Critical thinking and data interpretation skills.
• Excellent communication and stakeholder management skills.
• Ability to translate complex data into actionable business insights.
• High attention to detail and commitment to data quality.
• Strong collaboration, documentation, and organizational skills.

Education:

• Bachelor's degree in Data Analytics, Data Engineering, Computer Science, Information Technology, Statistics, Mathematics, or a related quantitative discipline.

Experience:

• Minimum 3 years of experience in Data Analytics, Data Engineering, Data Visualization, or Data Management.
• At least 2 years of hands-on experience designing and developing dashboards.
• Minimum 2 - 3 years of experience writing complex SQL queries and working with relational databases (PostgreSQL preferred).
• Minimum 2 - 3 years of experience using Python for data manipulation, transformation, and automation.
• Experience with data modeling, predictive analytics, or machine learning is desirable.