Lead Data Engineer (Banking)

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.

Core data engineering

• 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.

Core data engineering

• 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