Backend Data Platform Engineer (SQL / Python /PySpark)
Posted
Summary
Builds and optimises batch, streaming, CDC and API-based ETL pipelines, historical data architectures, and analytical datasets for an enterprise banking/payments data platform, with SQL tuning, Spark job optimisation, and data quality reconciliation. Core stack: SQL, Python, PySpark, Teradata, Hadoop/Cloudera, Kafka, Airflow.
About the Role
We are looking for a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms. In this role, you will independently build, optimise, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality and reconciliation across our ecosystem.
Responsibilities Pipeline Engineering:
Develop and maintain robust batch, streaming, CDC, and API-based data pipelines, historical snapshots, and SCD frameworks. Data Transformation:
Build transformations for EDW, data lakes, and semantic layers, including specialised payment harmonisation datasets. Performance & Quality:
Tune SQL queries and Spark jobs, implementing stringent reconciliation controls, error handling, and operational monitoring. SDLC Delivery:
Support SIT, UAT, defect resolution, release execution, and production verification in close collaboration with architecture and BI teams.
Requirements: Experience:
Minimum 6 years of hands-on data engineering experience, with independent ownership of development and testing deliverables. Technical Stack:
Advanced proficiency in
SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera
ecosystems. Tools & Integration:
Experience with Informatica/Talend, Hive, Impala, Kafka, Airflow, APIs, and Git-based version control. Domain Exposure:
Strong background in enterprise data warehousing, data lakes, and banking data operations (Payments, Risk, Finance, or AML preferred). Core Competencies:
Independent ownership of deliverables, analytical mindset, and a commitment to secure, high-quality engineering standards.
Responsibilities Pipeline Engineering:
Develop and maintain robust batch, streaming, CDC, and API-based data pipelines, historical snapshots, and SCD frameworks. Data Transformation:
Build transformations for EDW, data lakes, and semantic layers, including specialised payment harmonisation datasets. Performance & Quality:
Tune SQL queries and Spark jobs, implementing stringent reconciliation controls, error handling, and operational monitoring. SDLC Delivery:
Support SIT, UAT, defect resolution, release execution, and production verification in close collaboration with architecture and BI teams.
Requirements: Experience:
Minimum 6 years of hands-on data engineering experience, with independent ownership of development and testing deliverables. Technical Stack:
Advanced proficiency in
SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera
ecosystems. Tools & Integration:
Experience with Informatica/Talend, Hive, Impala, Kafka, Airflow, APIs, and Git-based version control. Domain Exposure:
Strong background in enterprise data warehousing, data lakes, and banking data operations (Payments, Risk, Finance, or AML preferred). Core Competencies:
Independent ownership of deliverables, analytical mindset, and a commitment to secure, high-quality engineering standards.