Lead Data Engineer (Banking)

Description

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