Role Description
The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and architectures that enable efficient data collection, processing, and analysis. This role ensures that high-quality, reliable data is available to support business intelligence, analytics, and machine learning initiatives. The ideal candidate is technically strong, detail-oriented, and passionate about building robust data systems that transform raw data into actionable insights.
Key Responsibilities
* Design, develop, and optimize data pipelines, ETL/ELT processes, and workflows for structured and unstructured data.
* Build and maintain scalable data architectures that support data warehousing, analytics, and reporting needs.
* Integrate data from multiple sources such as APIs, databases, and third-party systems into centralized data platforms.
* Collaborate with data analysts, data scientists, and business teams to understand data requirements and ensure data accuracy and availability.
* Develop and enforce best practices for data governance, security, and quality assurance.
* Monitor, troubleshoot, and optimize data processes for performance and cost efficiency.
* Implement data validation, cleansing, and transformation procedures to maintain data integrity.
* Work with cloud platforms (e.g., AWS, Azure, GCP) to manage data storage, orchestration, and automation tools.
* Create and maintain documentation for data models, data flow diagrams, and pipeline configurations.
* Support the development of analytics and machine learning pipelines by providing clean and well-structured datasets.
* Collaborate with DevOps teams to deploy, scale, and maintain data infrastructure in production environments.
* Continuously improve data engineering practices through automation, monitoring, and innovation.
* Stay updated on emerging technologies and trends in data architecture, big data, and cloud computing.
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 database development.
* Strong proficiency in SQL and at least one programming language (Python, Java, or Scala preferred).
* Hands-on experience with ETL tools and frameworks (e.g., Apache Airflow, dbt, or Talend).
* Experience with big data technologies such as Spark, Hadoop, or Kafka.
* Familiarity with cloud-based data services (AWS Redshift, Google BigQuery, Azure Synapse, or Snowflake).
* Solid understanding of data modeling, schema design, and database management (relational and NoSQL).
* Knowledge of APIs, data integration, and data streaming methodologies.
* Strong problem-solving, analytical, and debugging skills.
* Excellent collaboration and communication abilities to work cross-functionally.
* Experience with containerization tools (Docker, Kubernetes) and CI/CD pipelines is a plus.
* Commitment to building efficient, scalable, and reliable data systems that support business growth.