Senior Data Engineer

- Design, develop, and maintain scalable data pipelines and ETL processes to support data ingestion, transformation, and loading from various sources. - Collaborate with cross-functional teams to understand data requirements and design efficient data models and schema for storage in data warehouses or other data storage solutions. - Optimize data workflows and processes to ensure high performance, reliability, and scalability of data infrastructure. - Monitor and troubleshoot data infrastructure to identify and resolve issues proactively, ensuring minimal disruption to data workflows. - Conduct performance tuning and optimization of database queries, data processing jobs, and data storage solutions for improved efficiency. - Stay updated on emerging technologies, tools, and trends in data engineering and recommend innovative solutions to enhance data capabilities and drive business value. - Document data architecture, processes, and workflows to facilitate knowledge sharing and ensure scalability and maintainability of data solutions. - Collaborate closely with data analysts to understand analytical requirements and provide support in data exploration, visualization, and interpretation to derive actionable insights for the business.

- Bachelor's degree or higher in Computer Science, Information Technology, Engineering, Mathematics, or a related field. - Proven experience (3+ years) working as a data engineer, software engineer, or a related role, preferably in the finance or banking industry. - Strong proficiency in SQL and other programming languages such as Python, Java, or Scala, etc., with the ability to write efficient, clean, and maintainable code. - Solid understanding of database systems, data warehousing concepts, and proficiency in SQL databases like Oracle, PostgreSQL, MySQL, SQL Server,... - Knowledge of data modeling principles and experience designing efficient data schemas for relational and non-relational databases. - Strong analytical and problem-solving skills with the ability to troubleshoot and resolve complex data-related issues. - Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders. - Experience with version control systems such as Git and proficiency in Linux/Unix environments. - Continuous learning mindset with a willingness to stay updated on emerging technologies and best practices in data engineering and analytics. - Experience with big data technologies and frameworks such as Hadoop, Spark, Kafka, or Flink for large-scale data processing and analytics. - Experience designing and implementing scalable data pipelines and ETL processes using tools like Apache Airflow, Apache NiFi, or similar. - Familiarity with cloud platforms such as AWS, Google Cloud Platform, or Azure, and proficiency in services like AWS Glue, EMR, Redshift, or equivalent. - Expertise in data visualization tools such as Tableau, Power BI, or matplotlib/seaborn for creating insightful visualizations and reports to communicate findings to stakeholders.