Role Description
The Data Engineer is responsible for designing, building, and maintaining scalable data infrastructure and pipelines that enable efficient data collection, storage, and analysis. This role focuses on transforming raw data into well-structured and accessible datasets for business intelligence, analytics, and machine learning applications. The Data Engineer collaborates closely with data scientists, analysts, and software teams to ensure data quality, reliability, and performance. The ideal candidate has strong technical expertise in database systems, data integration, and modern cloud-based data architectures. This position offers the opportunity to shape data-driven decision-making and contribute to the organization's digital transformation strategy.
Key Responsibilities
* Design, develop, and maintain efficient ETL/ELT pipelines to extract, transform, and load data from multiple sources.
* Build and manage data models, warehouses, and lakes to support analytical and reporting needs.
* Optimize data storage and retrieval processes for scalability, speed, and reliability.
* Collaborate with data scientists, analysts, and business stakeholders to define data requirements and ensure accessibility.
* Implement data validation, quality control, and monitoring processes to ensure accuracy and consistency.
* Integrate data from APIs, databases, cloud platforms, and external systems into unified data repositories.
* Support automation of data workflows and develop reusable data integration frameworks.
* Monitor and maintain data infrastructure performance, ensuring minimal downtime and high availability.
* Ensure compliance with data governance, privacy, and security standards (GDPR, ISO, etc.).
* Participate in system design, documentation, and technical reviews to maintain best engineering practices.
* Research and evaluate emerging data engineering tools and technologies for process improvement.
Qualifications
* Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
* 2–5 years of experience in data engineering, ETL development, or database management.
* Proficiency in SQL and programming languages such as Python, Scala, or Java.
* Strong experience with data pipeline frameworks and orchestration tools (Airflow, Prefect, or Luigi).
* Familiarity with cloud-based data platforms such as AWS (Redshift, Glue, S3), Azure (Data Factory, Synapse), or Google Cloud (BigQuery, Dataflow).
* Experience with both relational and non-relational databases (PostgreSQL, MySQL, MongoDB, Cassandra, etc.).
* Knowledge of data modeling, schema design, and data warehousing principles.
* Understanding of distributed data processing frameworks (Spark, Hadoop, or Kafka).
* Strong analytical, problem-solving, and troubleshooting skills.
* Excellent teamwork and communication abilities in cross-functional environments.
* Familiarity with CI/CD pipelines, DevOps, and infrastructure-as-code (IaC) is a plus.
* Passion for data-driven innovation and continuous learning in emerging technologies.