Data Engineer

Role Overview

The Data Engineer is responsible for designing, building, and maintaining robust data pipelines and infrastructure that enable efficient data collection, storage, transformation, and analysis. This role ensures the accessibility, accuracy, and reliability of data used across business intelligence, analytics, and machine learning initiatives. Working closely with data scientists, analysts, and software engineers, the Data Engineer plays a key role in transforming raw data into actionable insights that drive data-driven decision-making. The position is ideal for individuals with strong technical expertise, analytical thinking, and a passion for data architecture and system optimization.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines to collect, process, and transform large datasets from various sources.
• Build and manage data architectures, including data lakes, warehouses, and streaming systems.
• Ensure data integrity, quality, and consistency across all storage and processing layers.
• Collaborate with data analysts, scientists, and business teams to define data requirements and optimize workflows.
• Implement ETL (Extract, Transform, Load) processes and automate data integration tasks.
• Optimize data storage and retrieval performance through database tuning and partitioning strategies.
• Develop APIs and data access tools to enable secure and efficient data sharing.
• Monitor data systems to ensure reliability, security, and compliance with data governance standards.
• Maintain documentation for data models, processes, and pipelines.
• Evaluate and integrate new technologies to improve data infrastructure scalability and performance.
• Support cloud data environments and tools, ensuring cost-effective and high-performance operations.
• Troubleshoot and resolve issues related to data ingestion, transformation, and delivery.
• Work within Agile or DevOps frameworks to deliver iterative improvements to data systems.

Qualifications and Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 2–5 years of experience in data engineering, data architecture, or database development.
• Proficiency in programming languages such as Python, Java, or Scala for data processing.
• Strong understanding of SQL and experience with relational and non-relational databases (PostgreSQL, MySQL, MongoDB, Cassandra, etc.).
• Experience with ETL tools and frameworks (Apache Airflow, AWS Glue, Talend, or dbt).
• Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
• Familiarity with cloud platforms like AWS, Azure, or Google Cloud (Redshift, BigQuery, Snowflake, or Databricks).
• Understanding of data modeling, warehousing, and schema design.
• Proficiency in version control systems (Git) and CI/CD practices.
• Strong problem-solving skills and attention to detail with the ability to work on complex data systems.
• Excellent communication and collaboration skills for cross-functional teamwork.

Summary

The Data Engineer plays a critical role in enabling data-driven strategies by ensuring the seamless flow and accessibility of high-quality data across the organization. Through the design of efficient data infrastructures and automation processes, this role supports analytics, machine learning, and business intelligence initiatives. Ideal for technically skilled and detail-oriented professionals, the Data Engineer position offers the opportunity to work with cutting-edge technologies and contribute to the foundation of an organization’s data ecosystem.