What you'd actually do

Build and maintain end-to-end batch and streaming data pipelines (ingestion → transformation → curation → serving) on AWS.

Develop scalable transformations using Spark (PySpark/Scala) and SQL, optimizing for performance, reliability, and cost.

Implement and optimize Snowflake solutions (schemas, tables, views, micro-partitioning/clustering considerations, warehouse sizing, query tuning, data sharing patterns where applicable).

Create robust orchestration and scheduling (e.g., Airflow/MWAA, AWS Step Functions, etc.) with monitoring, alerting, retries, and operational runbooks.

Utilize AI-powered tools and large language models (LLMs) to accelerate data pipeline development, automate code generation, and streamline debugging and documentation processes.

Skills

Required

Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry.
Strong SQL skills (complex joins, window functions, query optimization, performance troubleshooting)
AWS data services and cloud-native patterns (e.g., S3, IAM, KMS, Glue, Lambda, EMR, Step Functions, Kinesis/MSK—relevant mix).
Solid understanding of data modeling
Practical experience with Snowflake (data loading, transformations, optimization, access controls).
Proficiency in Python (or equivalent) for pipeline development, automation, and testing.
Experience with Git and CI/CD practices
familiarity with engineering standards for code reviews and automated testing.

Nice to have

Prior experience of developing solutions for Financial domain
Exposure to distributed model training, and deployment
Familiarity with techniques for model explainability and self-validation
3+ years of hands-on experience in Data Engineering (preferably in banking/financial services or other regulated environments).
Hands-on development experience with Spark (PySpark preferred) for large-scale processing.

Other signals

design autonomous AI agents

leverage advanced data analysis, statistical modeling, and AI/ML techniques

cloud-centric software delivery

Utilize AI-powered tools and large language models (LLMs) to accelerate data pipeline development, automate code generation, and streamline debugging and documentation processes

Apply AI-driven techniques for data quality validation, anomaly detection, and automated data profiling

design, build, and maintain scalable data infrastructure that supports the training, deployment, and monitoring of AI/ML models

As a Senior Applied AI/ML Associate within our dynamic team of innovators and technologists, you will revolutionize how the Bank services and advises clients, deepen client engagements, and promote process transformation. You will analyze existing processes and vast amounts of data to design autonomous AI agents. We seek individuals passionate about leveraging advanced data analysis, statistical modeling, and AI/ML techniques to solve complex business challenges through high-quality, cloud-centric software delivery. Our culture thrives on experimentation, continuous improvement, and learning. You will work in a collaborative, trusting, and intellectually stimulating environment—one that values diversity of thought and fosters creative solutions that serve the best interests of our global clientele.

 **Job Responsibilities**

Build and maintain end-to-end batch and streaming data pipelines (ingestion → transformation → curation → serving) on AWS.
Develop scalable transformations using Spark (PySpark/Scala) and SQL, optimizing for performance, reliability, and cost. Implement and optimize Snowflake solutions (schemas, tables, views, micro-partitioning/clustering considerations, warehouse sizing, query tuning, data sharing patterns where applicable).
Understanding of data models for enterprise analytics (dimensional/star schemas, normalized models; applying standard banking data concepts where relevant). Create robust orchestration and scheduling (e.g., Airflow/MWAA, AWS Step Functions, etc.) with monitoring, alerting, retries, and operational runbooks.
Ensure data quality through validation checks, reconciliation, and controls (e.g., completeness, timeliness, accuracy, deduplication). Implement security and governance controls suitable for banking: encryption, least-privilege access, auditability, data retention, and lineage/documentation.
Collaborate with stakeholders across technology, risk, compliance, analytics, and product to translate requirements into reusable, well-defined datasets and support production operations: incident triage, root-cause analysis, performance tuning, and continuous improvement of pipeline stability.
Utilize AI-powered tools and large language models (LLMs) to accelerate data pipeline development, automate code generation, and streamline debugging and documentation processes. Apply AI-driven techniques for data quality validation, anomaly detection, and automated data profiling to ensure the integrity and reliability of data assets.
Collaborate with data science and ML teams to design, build, and maintain scalable data infrastructure that supports the training, deployment, and monitoring of AI/ML models. Continuously evaluate and adopt emerging AI technologies to optimize ETL workflows, reduce manual effort, and enhance overall engineering productivity.

Required qualifications, capabilities, and skills

Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry.
Strong SQL skills (complex joins, window functions, query optimization, performance troubleshooting) and experience with AWS data services and cloud-native patterns (e.g., S3, IAM, KMS, Glue, Lambda, EMR, Step Functions, Kinesis/MSK—relevant mix).
Solid understanding of data modeling and how model choices impact performance, usability, and governance.
Practical experience with Snowflake (data loading, transformations, optimization, access controls). Proficiency in Python (or equivalent) for pipeline development, automation, and testing.
Experience with Git and CI/CD practices; familiarity with engineering standards for code reviews and automated testing.

Preferred qualifications, capabilities, and skills

Prior experience of developing solutions for Financial domain
Exposure to distributed model training, and deployment
Familiarity with techniques for model explainability and self-validation
3+ years of hands-on experience in Data Engineering (preferably in banking/financial services or other regulated environments).
Hands-on development experience with Spark (PySpark preferred) for large-scale processing.

 **Job Responsibilities**

Build and maintain end-to-end batch and streaming data pipelines (ingestion → transformation → curation → serving) on AWS.
Develop scalable transformations using Spark (PySpark/Scala) and SQL, optimizing for performance, reliability, and cost. Implement and optimize Snowflake solutions (schemas, tables, views, micro-partitioning/clustering considerations, warehouse sizing, query tuning, data sharing patterns where applicable).
Understanding of data models for enterprise analytics (dimensional/star schemas, normalized models; applying standard banking data concepts where relevant). Create robust orchestration and scheduling (e.g., Airflow/MWAA, AWS Step Functions, etc.) with monitoring, alerting, retries, and operational runbooks.
Ensure data quality through validation checks, reconciliation, and controls (e.g., completeness, timeliness, accuracy, deduplication). Implement security and governance controls suitable for banking: encryption, least-privilege access, auditability, data retention, and lineage/documentation.
Collaborate with stakeholders across technology, risk, compliance, analytics, and product to translate requirements into reusable, well-defined datasets and support production operations: incident triage, root-cause analysis, performance tuning, and continuous improvement of pipeline stability.
Utilize AI-powered tools and large language models (LLMs) to accelerate data pipeline development, automate code generation, and streamline debugging and documentation processes. Apply AI-driven techniques for data quality validation, anomaly detection, and automated data profiling to ensure the integrity and reliability of data assets.
Collaborate with data science and ML teams to design, build, and maintain scalable data infrastructure that supports the training, deployment, and monitoring of AI/ML models. Continuously evaluate and adopt emerging AI technologies to optimize ETL workflows, reduce manual effort, and enhance overall engineering productivity.