Lead Data Engineer (Banking)

Unison Group; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

Unison Group is seeking a Lead Data Engineer (Banking) to own production delivery of data pipelines inside banks, handling customer, account, transaction, payments and AML data. You will work within release, scheduling and change controls to ensure timely, accurate data flows.

Ideal candidates have 12+ years of experience (6+ in banking) and hands-on expertise with Teradata SQL, Spark, PySpark, Hive, and data automation frameworks. Leadership and cross-team collaboration are essential.

Qualifications

• Lead: 12+ years, including 6+ in banking.
• Senior: 8+ years, including 4+ in banking
• Extensive hands-on experience delivering data pipelines inside financial institutions

Responsibilities

• Lead production delivery of data pipelines inside banks and ensure schedule adherence
• Oversee customer, account, transaction, payments and AML data processing
• Manage bank release, scheduling and change controls
• Perform data modeling and performance tuning for large-scale datasets
• Coordinate ETL/ELT development, CDC, and metadata-driven automation
• Ensure data quality, reconciliation, and SLA monitoring
• Work with Spark, Hadoop, and SQL-on-Hadoop stack across Teradata, Iceberg, Trino
• Collaborate with cross-functional teams to deliver end-to-end data solutions
• Mentor engineers and drive best practices in data governance

Skills

Leadership

Banking domain knowledge

Data engineering

Tools

Teradata SQL

BTEQ

TPT

FastLoad/MultiLoad

PySpark

Spark

Hive

Impala

HBase

Pig

Hue

Iceberg

Trino

Python

R

SQL tuning

Job description

• Production delivery of data pipelines inside banks


• Customer, account, transaction, payments and AML data


• Working within bank release, scheduling and change controls


• Teradata SQL, BTEQ, TPT and FastLoad/MultiLoad; query tuning


• Spark (PySpark/Scala), Hive, Impala, HBase, Pig and Hue; Apache Iceberg and Trino; Python and R


• Historical data processing: snapshots, SCD Type 2 and multi-grain backfills with control totals and validation reports


• ETL/ELT development, CDC, metadata-driven automation frameworks and parameter-driven SQL generation


• Reconciliation, data quality and SLA monitoring


• Performance at scale: tables of 1 billion+ rows and multi-year history

Role: Lead Data Engineer (Banking)

Must-have Skills

Banking and domain

• Production delivery of data pipelines inside banks


• Customer, account, transaction, payments and AML data


• Working within bank release, scheduling and change controls


• Teradata SQL, BTEQ, TPT and FastLoad/MultiLoad; query tuning


• Spark (PySpark/Scala), Hive, Impala, HBase, Pig and Hue; Apache Iceberg and Trino; Python and R


• Historical data processing: snapshots, SCD Type 2 and multi-grain backfills with control totals and validation reports


• ETL/ELT development, CDC, metadata-driven automation frameworks and parameter-driven SQL generation


• Reconciliation, data quality and SLA monitoring


• Performance at scale: tables of 1 billion+ rows and multi-year history

Core data engineering

• Production delivery of data pipelines inside banks


• Customer, account, transaction, payments and AML data


• Working within bank release, scheduling and change controls


• Teradata SQL, BTEQ, TPT and FastLoad/MultiLoad; query tuning


• Spark (PySpark/Scala), Hive, Impala, HBase, Pig and Hue; Apache Iceberg and Trino; Python and R


• Historical data processing: snapshots, SCD Type 2 and multi-grain backfills with control totals and validation reports


• ETL/ELT development, CDC, metadata-driven automation frameworks and parameter-driven SQL generation


• Reconciliation, data quality and SLA monitoring


• Performance at scale: tables of 1 billion+ rows and multi-year history

Requirements

Experience

• Lead: 12+ years, including 6+ in banking.


• Senior: 8+ years, including 4+ in banking

Banking and domain

• Production delivery of data pipelines inside banks


• Customer, account, transaction, payments and AML data


• Working within bank release, scheduling and change controls


• Teradata SQL, BTEQ, TPT and FastLoad/MultiLoad; query tuning


• Spark (PySpark/Scala), Hive, Impala, HBase, Pig and Hue; Apache Iceberg and Trino; Python and R


• Historical data processing: snapshots, SCD Type 2 and multi-grain backfills with control totals and validation reports


• ETL/ELT development, CDC, metadata-driven automation frameworks and parameter-driven SQL generation


• Reconciliation, data quality and SLA monitoring


• Performance at scale: tables of 1 billion+ rows and multi-year history

Core data engineering

• Production delivery of data pipelines inside banks


• Customer, account, transaction, payments and AML data


• Working within bank release, scheduling and change controls


• Teradata SQL, BTEQ, TPT and FastLoad/MultiLoad; query tuning


• Spark (PySpark/Scala), Hive, Impala, HBase, Pig and Hue; Apache Iceberg and Trino; Python and R


• Historical data processing: snapshots, SCD Type 2 and multi-grain backfills with control totals and validation reports


• ETL/ELT development, CDC, metadata-driven automation frameworks and parameter-driven SQL generation


• Reconciliation, data quality and SLA monitoring


• Performance at scale: tables of 1 billion+ rows and multi-year history

Integration and platforms

• Kafka, Spark Streaming, Informatica (PowerCenter, IDMC, IDL) and Talend; REST API development


• Denodo, Snowflake and NoSQL databases


• Airflow, Control-M or Autosys; Git, CI/CD, GitOps and Kubernetes

Delivery and communication (Lead)

• Framework design, code standards, code reviews and estimation


• Guiding a team of engineers and working with architects and analysts

Good-to-have Skills

• Databricks: Delta Lake, Unity Catalog and Workflows


• Data services on Azure, Google Cloud, Huawei Cloud or Alibaba Cloud


• Data modelling for Qlik Sense or Power BI

Certifications (preferred)

Teradata Vantage; Cloudera Data Engineer; Databricks Data Engineer Associate or Professional