Industry: Banking and financial services · Level: Mid to Senior · Experience: Mid 4–6 years, Senior 7+ years
Employment type: Fixed-term contract of at least 12 months, with a strong possibility of extension or conversion to permanent · Rate: Competitive hourly rate
Summary
Our client, a leading financial services institution, is looking for an AWS Data Engineer to build and run the cloud data pipelines and platforms behind risk, finance, fraud and customer analytics. You will turn large volumes of transactional and customer data into trusted, governed datasets while meeting strict security and regulatory requirements.
Minimum qualifications
Bachelor's degree or National Diploma (NQF level 6+) in Computer Science, Information Systems, Engineering, Mathematics or a related field
4+ years of data engineering experience for mid-level; 7+ years for senior
At least 2 years of hands-on AWS data platform experience
Experience in banking, insurance or another regulated financial environment
Certifications
AWS Certified Data Engineer – Associate (strongly preferred)
AWS Certified Solutions Architect – Associate (advantageous)
AWS Certified Security – Specialty (advantageous for senior)
Databricks or Snowflake certification (advantageous)
DAMA CDMP or similar data management certification (advantageous)
Duties
Design, build and maintain batch and streaming data pipelines on AWS
Develop ETL/ELT processes using AWS Glue, Lambda, Step Functions and Spark
Build and optimise data lakes and warehouses on S3, Redshift, Athena and Lake Formation
Ingest data from core banking, payments, CRM and third-party sources, including real-time feeds via Kinesis or MSK/Kafka
Implement data quality checks, lineage, cataloguing and monitoring
Apply encryption, access control and masking to protect sensitive customer and financial data
Automate infrastructure and deployments using Infrastructure as Code and CI/CD
Tune pipeline performance and manage cloud cost
Work with data scientists, analysts and business teams to deliver reliable datasets
Senior level: define data architecture and standards, lead engineers and make platform design decisions
Requirements
Technical skills
Python and advanced SQL; PySpark; Scala or Java (advantageous)
AWS services: S3, Glue, Redshift, Athena, EMR, Lambda, Kinesis, DMS, Lake Formation, IAM, KMS, CloudWatch
Data modelling: dimensional modelling, data vault, lakehouse patterns
Orchestration with Airflow (MWAA) or Step Functions
Infrastructure as Code: Terraform or CloudFormation
Git, CI/CD pipelines, automated testing for data pipelines
Experience with Databricks, Snowflake or Apache Iceberg/Hudi (advantageous)
Financial services knowledge
Banking data domains such as payments, accounts, lending, risk and finance
Data governance, lineage and retention; POPIA, BCBS 239, PCI DSS and audit requirements
Experience supporting regulatory and management reporting (advantageous)
Personal attributes
Strong analytical mindset with a focus on data accuracy and reliability
Able to explain technical trade-offs to non-technical stakeholders
Works well in agile, cross-functional delivery teams
Skills
AWS, Python, SQL, PySpark, Scala, AWS Glue, Amazon S3, Redshift, Athena, EMR, Lambda, Kinesis, AWS DMS, Lake Formation, IAM, KMS, CloudWatch, Step Functions, Airflow, MWAA, Terraform, CloudFormation, ETL, ELT, Data Lake, Data Warehouse, Data Modelling, Dimensional Modelling, Data Vault, Lakehouse, Databricks, Snowflake, Apache Iceberg, Kafka, Data Quality, Data Governance, Data Lineage, CI/CD, Git, BCBS 239, POPIA, PCI DSS, Banking
4 years