The AI-Native Data Engineer builds and maintains reliable data pipelines, integrations, and data solutions for digital banking applications and enterprise platforms. Working with architects, technical leads, and development teams, the role turns business and technical requirements into secure, scalable, business-ready data while using AI-assisted development to improve engineering productivity and support data-driven products.

Responsibilities

Pipeline development and management: build, optimize, monitor, and recover batch/real-time ETL workflows.
Data integration and transformation: connect banking platforms, map and validate data, resolve data-flow issues.
Data quality/governance: implement checks, reconciliation, monitoring, secure processing, lineage documentation.
AI-native engineering: leverage reviewed LLMs for coding, documentation, quality, and automation.
Testing/release support: test pipelines and journeys, troubleshoot defects, support deployments, monitor performance.
Collaboration/technical execution: align architecture, define interfaces, coordinate teams, review designs.
Engineering excellence: apply standards, optimize performance, create reusable components, and automate improvements.

Required Qualifications

At least 3 years of hands on experience in AWS, Azure, GCP, or other cloud data platforms.
Hands-on experience building and maintaining data pipelines and ETL/ELT processes.
Strong relational database, SQL, data modeling, and performance optimization knowledge.
Experience integrating enterprise systems through APIs, messaging, databases, or equivalent mechanisms.
Understanding of data quality, validation, integrity, and governance practices.
Experience using AI-assisted tools, LLMs, or automation to improve engineering workflows.

Preferred Qualifications

Familiarity with Spark, Kafka, Airflow, or equivalent data processing tools.
Familiarity with CI/CD, version control, automated testing, and DevOps practices.
Strong analytical, problem-solving, communication, and collaboration skills.