What you will be doing
Design, build, and maintain robust data pipelines that connect diverse data sources, including databases, APIs, cloud platforms, and third-party systems. You will be responsible for data extraction, transformation, and loading (ETL) processes, ensuring data accuracy, consistency, and reliability
Collaborate with cross-functional teams to architect and optimize data storage and retrieval systems. Implement data warehousing solutions that support efficient data querying and analysis while adhering to best practices in data modeling
Monitor data quality and establish data governance practices to maintain high data integrity. Work on data cleansing, data validation, and data profiling to ensure the accuracy and completeness of data
Continuously optimize data pipelines and database performance to enhance overall data processing efficiency. Identify and resolve bottlenecks and performance issues within the data infrastructure
Stay updated with the latest data integration tools and technologies. Evaluate and recommend suitable tools and platforms to enhance data integration processes
Your skills and experience
Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or a related field
Proven experience as a Data Engineer, with a strong focus on data integration and ETL processes
Proficiency in programming languages like Python for data manipulation and integration tasks
Strong understanding of data modeling concepts and database technologies such as SQL, NoSQL, and Big Data solutions
Familiarity with data integration tools and cloud-based data platforms, such as Databricks, AirFlow, Kafka, Spark, AWS, Glue – a must
Knowledge of data governance, data security, and data privacy principles
Excellent problem-solving skills and the ability to troubleshoot and resolve data-related issues
Strong communication and collaboration skills to work effectively with cross-functional teams, including data scientists, analysts, and business stakeholders