Data Engineer
- Design, develop, and maintain scalable data pipelines using Hadoop, Apache Spark, and related Big Data technologies.
- Implement ETL (Extract, Transform, Load) processes to manage large-scale data from various sources.
- Collaborate with cross-functional teams to define data architecture, data models, and pipelines that support banking applications and analytics.
- Develop and optimize complex SQL queries for data extraction, aggregation, and transformation.
- Work with business stakeholders to understand banking requirements and translate them into technical specifications for data solutions.
- Implement data quality controls and performance optimization techniques for high-volume data systems.
- Ensure adherence to data governance, security, and compliance guidelines in the banking industry.
- Continuously monitor and improve the performance of existing data solutions, including troubleshooting and debugging issues.
- Contribute to the integration of new tools and technologies in the data ecosystem to support ongoing projects and innovation.
- Experience in data engineering, with a focus on Hadoop and Apache Spark.
Key skills
- Hadoop, Spark, SQL, ETL, Kafka, Hive, HBase
- Strong experience in SQL, including complex queries, optimization, and performance tuning.
- Experience in the banking or financial services industry is highly preferred.
- Proficiency in data warehousing concepts and hands-on experience with ETL tools.
- Familiarity with distributed systems and large-scale data processing.
- Solid experience with Linux/Unix scripting and working knowledge of shell scripting.
- Experience with cloud platforms such as AWS, Azure, or GCP is a plus.
- Knowledge of Kafka, Hive, HBase, or other Big Data tools is an advantage.
- Excellent problem-solving and debugging skills.
Interested candidates, please send your CVs on [email protected]
Regret to inform that only shortlisted candidates will be notified.
CEI: R1988671
EA License: 14C7275
