A Data Engineer is responsible for designing, building, and maintaining systems that collect, store, and process large volumes of data so it can be used for analytics, reporting, and machine learning. They develop and manage data pipelines and architectures that enable organizations to make data-driven decisions. Data Engineers ensure that data is reliable, accessible, and efficiently processed.

In this role, you will build and maintain ETL and ELT pipelines, automate data ingestion from multiple sources such as APIs, databases, and files, and design data warehouses and data lakes using systems like Amazon Redshift, Google BigQuery, or Snowflake. You will optimize SQL queries and manage both relational and NoSQL databases, ensuring high performance and reliability. You will also process and transform large datasets using frameworks like Apache Spark and Hadoop, maintain data accuracy and consistency, and monitor pipelines to quickly resolve any issues. Collaboration with data scientists, analysts, and software engineers is an important part of the role, as is supporting business intelligence tools and dashboards.

The role requires strong programming skills in Python, Java, or Scala, advanced SQL knowledge, and experience with ETL tools such as Apache Airflow or dbt. Familiarity with cloud platforms like AWS, GCP, or Azure, data warehousing concepts, distributed systems, and streaming tools such as Apache Kafka is essential. Nice-to-have skills include a basic understanding of machine learning, experience with data visualization tools such as Tableau or Power BI, and familiarity with DevOps practices including CI/CD, Docker, and Kubernetes.

Candidates should have a bachelor’s degree in Computer Science, IT, or a related field, along with one to five or more years of relevant experience. Strong problem-solving abilities, attention to detail, effective communication, and the ability to work with large and complex datasets are essential for success in this role.