Top Data & Analytics Jobs

Design, build, and maintain scalable Spark/SQL data pipelines and ETL/ELT frameworks for batch and real-time processing. Deploy and manage containerized data workloads on Docker/Kubernetes/OpenShift, manage data lakes on object storage (S3/ADLS) using formats like Apache Iceberg, implement CI/CD for data workflows, optimize Spark performance, drive architecture and modernization, ensure governance/compliance, and provide technical leadership and mentorship.

Lead and grow a 15-person engineering team to deliver enterprise data, analytics, and automation initiatives. Own end-to-end delivery, technical direction, architecture reviews, and modernization across ETL, data integration, warehousing, and cloud platforms while ensuring regulatory compliance, quality, and operational excellence. Partner with stakeholders to prioritize roadmaps and drive Agile practices, observability, and automation.

Design, build, and maintain scalable data ingestion pipelines across on-premises and cloud. Develop data models for analytics, ensure data quality, governance, security, and compliance. Collaborate with analysts and data scientists, apply big data technologies and DevOps/CI-CD practices, implement monitoring and alerting, and participate in Agile/SAFe ceremonies.

Perform data analysis and support AMG business users by querying large-scale data (Hadoop/Hive/Impala/Spark), producing technical specifications and STM, maintaining data dictionaries and mappings, resolving metadata issues, and collaborating with Product Owners and developers within Agile/Scrum teams.

Designs, builds, and maintains scalable data services, ETL pipelines and data migration processes across batch and streaming platforms (Hadoop, Spark, Kafka). Collaborates with users, developers, operations and business partners to define requirements, ensure data accuracy/security, and support data lakehouse and warehousing solutions.

Designs, builds, and supports scalable data services and infrastructure across multiple platforms. Collaborates with users, developers, operations, and business partners to define requirements, perform data migration and transformation, ensure data accuracy/security, and maintain availability and scalability. On-call rotation and in-office presence required.

Manage design and delivery of reusable, AI-ready data products for retail-banking portfolios. Develop interoperable data-engineering artifacts, enable self-service analytics, maintain data marketplace, oversee data policies, integrity, budgeting, stakeholder alignment, and lead cross-functional teams in a matrixed environment.

Lead design and delivery of reusable, AI-ready data products and interoperable data-engineering artifacts for retail-banking portfolios. Manage data product lifecycles, ensure data integrity and governance, create self-service analytics, and align technology and business teams in a matrixed organization.

As Head of Investment Data, you will define and drive data strategy, governance, and operations to enhance investment decision-making and client outcomes.

The Data Engineer Senior role involves developing data solutions, leading analysis efforts, and designing data service infrastructures within financial services using various technologies.