Data Engineer

Role Summary 

We are looking for a Data Engineer to design, build, and optimize ingestion pipelines and data transformations for an AWS-based Lakehouse platform in a banking environment. 

This role focuses on implementing scalable data pipelines using Debezium, MSK, Flink, Python, AWS Lake Formation, and Redshift. The Data Engineer will support the transition from interval-based ingestion to near-real-time data processing, working under the technical direction of the Senior Data Engineer / Data Platform Lead. 

Key Responsibilities 

• Build and execute data ingestion pipelines using Debezium, MSK, Flink, and Python. 
• Support the transition from hourly batch ingestion to near-real-time ingestion within minutes. 
• Implement CDC, streaming, and batch data processing patterns. 
• Develop bronze, silver, and gold data transformations within the Lake Formation-based Lakehouse. 
• Configure and optimize Redshift tables, schemas, and data loading processes. 
• Work with the Senior Data Engineer / Data Platform Lead to ensure pipelines align with platform architecture and design standards. 
• Perform data quality checks, validation, reconciliation, and issue investigation. 
• Optimize pipeline performance, reliability, scalability, and cost efficiency. 
• Collaborate with data architects, engineers, business teams, and banking stakeholders to support reporting and analytics needs. 

Required Skills 

• Strong SQL and Python skills. 
• Hands-on experience building scalable data pipelines. 
• Experience with data ingestion, ETL/ELT, data transformation, and data processing. 
• Experience with CDC, streaming technologies, and near-real-time data pipelines. 
• Practical knowledge of AWS data services, Lakehouse architecture, and cloud-based data platforms. 
• Experience with Debezium, Kafka/MSK, Flink, AWS Lake Formation, Glue, S3, Athena, or Redshift. 
• Strong understanding of data quality validation, reconciliation, and performance optimization. 
• Ability to work effectively within a technical engineering team and follow architecture guidance. 

Domain Experience 

• Banking domain experience is a plus. 
• Experience with banking data, transaction data, customer/account data, regulatory reporting, financial reporting, AML/KYC, or risk data is highly valuable. 

Nice to Have 

• Experience with Apache Airflow or other orchestration tools. 
• Experience with data catalog, lineage, metadata, and governance practices. 
• Exposure to Databricks, Snowflake, BigQuery, Synapse, or similar modern data platforms. 
• Experience working in Agile delivery teams using Jira and Confluence. 

Key Qualifications 

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Data Analytics, or a related field. 
• 4+ years of experience in Data Engineering, Data Pipeline Development, ETL/ELT, or similar roles. 
• Proven experience building data pipelines in cloud or Lakehouse environments. 
• Experience in banking, financial services, or regulated industries is an advantage. 

(Note: Due to the high volume of applications we receive, we are unable to respond to every candidate individually. If you have not received a response from GFT regarding your application within 10 workdays, please consider that we have decided to proceed with other candidates. We truly appreciate your interest in GFT and thank you for your understanding)