Penta Consultancy Hiring! Full Time Data Engineer in Federal Territory - Ricebowl

About Our Client

Our client is a data-driven organization committed to leveraging analytics to support strategic decision-making across marketing and operations. With a strong focus on transforming complex data into actionable insights, the company emphasizes innovation, collaboration, and continuous improvement. By combining market intelligence, customer behavior analysis, and operational data, they aim to drive sustainable business growth and performance excellence.

Job Responsibilities

• Design, develop, and maintain scalable data pipelines (ETL/ELT) to support data ingestion, transformation, and processing across cloud environments (Azure/AWS).
• Build and optimize data infrastructure using tools such as Azure Data Factory, Databricks, or similar platforms.
• Develop and maintain efficient SQL queries, data models, and stored procedures to ensure performance, scalability, and data integrity.
• Design and implement data analytics and machine learning models, including anomaly detection, predictive analytics, and pattern recognition.
• Automate data workflows and monitoring processes using Python and SQL-based alerting systems.
• Perform data analysis and investigations to identify trends, anomalies, and root causes of data inconsistencies.
• Deploy, monitor, and maintain data pipelines and machine learning models in production environments.
• Implement CI/CD pipelines and version control practices using tools such as Azure DevOps or similar.
• Ensure data quality, governance, and reliability across all data processes.
• Participate in Agile ceremonies and contribute to continuous improvement of data workflows and systems.

Job Requirements

• Bachelor’s Degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Minimum 2 years of experience in data engineering, data science, or a related data-focused role.
• Proficient in Python (e.g. Pandas, NumPy, Scikit-learn) and SQL (complex queries, performance tuning).
• Hands-on experience with ETL/ELT processes and data pipeline development.
• Experience with cloud platforms such as Azure (Data Factory, Databricks, Azure SQL) or AWS.
• Solid understanding of data modeling, data warehousing, and normalization concepts.
• Knowledge of machine learning techniques (e.g. regression, classification, clustering, anomaly detection).
• Experience with CI/CD pipelines and version control tools (e.g. Azure DevOps, Git).
• Familiarity with data pipeline monitoring, troubleshooting, and optimization.
• Experience working in Agile environments using tools such as JIRA or Confluence.