Data Engineer

Data Pipeline Development & Integration

• Design, build, and maintain scalable ETL/ELT pipelines to extract, transform, and load data from multiple sources.
• Develop automated data integration solutions supporting enterprise applications, analytics, and reporting.
• Integrate structured and semi-structured data from APIs, relational databases, cloud platforms, and third-party systems.
• Develop batch and real-time data processing solutions.
• Troubleshoot pipeline failures, data inconsistencies, and performance bottlenecks.

Data Warehousing & Architecture

• Design and optimize enterprise data warehouses, data lakes, and lakehouse architectures.
• Develop efficient data models, schemas, and transformations to support business intelligence and analytics.
• Build and maintain cloud-based data solutions using AWS, Microsoft Azure, or Google Cloud.
• Optimize SQL queries, database performance, and data processing workloads.
• Implement data partitioning, indexing, and storage optimization strategies.

Data Quality & Governance

• Develop automated data validation, monitoring, and quality assurance processes.
• Ensure data accuracy, consistency, reliability, and availability across enterprise systems.
• Implement data governance standards, access controls, and security best practices.
• Maintain documentation for data pipelines, architecture, and transformation logic.
• Support compliance with organizational data privacy and security requirements.

Cloud Engineering & Automation

• Develop and maintain cloud-native data pipelines and processing workflows.
• Utilize orchestration tools such as Apache Airflow, Azure Data Factory, or similar technologies.
• Implement CI/CD practices for data engineering workflows.
• Automate data processing, monitoring, and infrastructure deployment.
• Collaborate with DevOps and infrastructure teams to improve scalability and reliability.

Analytics & AI Support

• Partner with business intelligence and analytics teams to deliver reliable datasets for reporting and dashboards.
• Develop curated data models supporting Power BI, Tableau, and other visualization platforms.
• Build data pipelines supporting machine learning, predictive analytics, and AI applications.
• Collaborate with AI engineers and data scientists to prepare, transform, and deliver data for model development and production applications.
• Identify opportunities to improve business processes through better data integration and automation.
• Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related field, or equivalent practical experience.
• 3–5+ years of professional experience in data engineering, database development, or related roles.
• Strong proficiency in SQL and Python.
• Hands-on experience developing ETL/ELT pipelines and data integration solutions.
• Experience working with relational databases such as SQL Server, PostgreSQL, Oracle, or MySQL.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of data warehousing concepts, dimensional modeling, and database optimization.
• Experience with data orchestration and scheduling tools.
• Familiarity with REST APIs, JSON, and other data integration methods.
• Experience with Git, CI/CD, and software development best practices.
• Strong analytical, troubleshooting, and communication skills.