Role Description
The Data Engineer is responsible for designing, building, and maintaining scalable data infrastructure and pipelines that enable efficient data collection, processing, and analysis across the organization. This role focuses on ensuring the integrity, availability, and quality of data used for analytics, reporting, and machine learning applications. The Data Engineer collaborates closely with data analysts, scientists, and software engineers to support business intelligence and data-driven decision-making.
Key Responsibilities
Design, develop, and manage robust data pipelines for ingesting, transforming, and storing structured and unstructured data.
Build and maintain ETL (Extract, Transform, Load) workflows to ensure high-quality and reliable data delivery.
Develop and optimize data architectures that support analytics, visualization, and predictive modeling.
Implement data validation, cleansing, and quality monitoring processes.
Collaborate with data analysts and scientists to ensure datasets are accurate, well-documented, and accessible.
Manage data integration between various systems, APIs, and databases.
Monitor and improve the performance, scalability, and reliability of data systems.
Implement best practices for data governance, privacy, and security.
Work with cloud platforms (AWS, Azure, or Google Cloud) for data storage, processing, and orchestration.
Stay current with emerging technologies and tools in data engineering and analytics.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
2–5 years of experience in data engineering, data warehousing, or big data technologies.
Strong programming skills in Python, SQL, or Scala.
Experience with data pipeline and workflow orchestration tools (e.g., Apache Airflow, Luigi, Prefect).
Proficiency with big data technologies such as Hadoop, Spark, Kafka, or Flink.
Hands-on experience with relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra).
Familiarity with cloud-based data services (AWS Redshift, Google BigQuery, Azure Data Factory, or Snowflake).
Knowledge of data modeling, schema design, and performance optimization.
Understanding of version control (Git) and CI/CD pipelines.
Strong analytical thinking, attention to detail, and problem-solving skills.
Excellent communication and teamwork abilities.
Experience with containerization (Docker, Kubernetes) and data API integration is a plus.
The ideal candidate is a technically skilled, detail-oriented, and collaborative professional with a passion for building reliable and scalable data solutions. The Data Engineer plays a critical role in enabling data-driven insights and innovation across the organization by ensuring that high-quality data is readily available for analytical and operational needs.