AI/ML Data Engineer - Wealth Management and Financial Services

Résumé du poste

Design and implement enterprise-scale cloud data platforms and scalable pipelines to support wealth management analytics. Develop AI/ML and GenAI solutions to enhance client insights and operational efficiency while ensuring data governance and quality.

Détails du poste

We are hiring for an AI/ML Data Engineer- Wealth Management and Financial Services. The role focuses on building cloud-based data platforms, scalable data pipelines, and AI/ML solutions supporting wealth analytics and financial data initiatives. A strong fit will have 8+ years of data engineering experience with Wealth Management or Financial Services experience and strong expertise in modern data and AI technologies. What You Bring 8+ years of Data Engineering experience, including experience within Wealth Management or Financial Institutions. Strong expertise with Snowflake, Databricks, Apache Spark, PySpark, and modern cloud data platforms. Advanced Python development for data pipelines, AI/ML solutions, automation, and API integrations. Experience building scalable ETL/ELT frameworks using dbt, DataStage, SQL, and cloud-native technologies. Hands-on experience with AWS services including S3, Lambda, SNS, and IAM. Experience with Kafka, Kinesis, Snowpipe, REST APIs, and real-time data ingestion frameworks. Deep SQL expertise including performance tuning, data modeling, data warehousing, and analytics engineering. Experience developing ML models for forecasting, customer analytics, attrition prediction, or financial analytics. Knowledge of GenAI, RAG architectures, Vector Databases, LangChain, LLM integration, and document intelligence solutions. Experience with Airflow, Autosys, CI/CD pipelines, Terraform, Kubernetes, Docker, and MLOps practices. Strong understanding of data governance, data quality, security, compliance, and financial reporting requirements. Excellent stakeholder management skills and ability to work with business, technology, and data leadership teams. What You'll Do Design and implement enterprise-scale data platforms supporting Wealth Management initiatives. Build and optimize data ingestion, transformation, and analytics pipelines processing high-volume financial data. Develop AI/ML and GenAI solutions that improve client insights, operational efficiency, and decision support. Implement data quality, observability, monitoring, and governance controls. Support cloud modernization, migration, and architecture initiatives. Collaborate with business stakeholders and technology teams to translate requirements into scalable solutions. Lead technical design discussions and provide mentorship to junior engineers. Nice to have Experience with Wealth Management platforms, investment products, portfolio analytics, and market data. Exposure to Elasticsearch, FAISS, Snowpark, Redshift, Aurora, and DB2. Experience converting legacy SAS/DataStage workloads into Spark or cloud-native architectures. Knowledge of Power BI, QuickSight, Tableau, or enterprise reporting platforms. Experience implementing enterprise GenAI and AI governance frameworks. MBA or advanced degree in Business, Data Science, Engineering, or related field. AWS or cloud certifications.

Ce que vous ferez

Design and implement enterprise-scale cloud data platforms and scalable pipelines to support wealth management analytics. Develop AI/ML and GenAI solutions to enhance client insights and operational efficiency while ensuring data governance and quality.

Exigences

Requires over 8 years of data engineering experience within financial services or wealth management, with deep expertise in Snowflake, Databricks, and Python. Candidates must have a strong background in building ETL/ELT frameworks, AWS cloud services, and implementing ML/GenAI architectures.

Compétences indiquées

• Kubernetes · Souhaitée
• SQL · Souhaitée
• Apprentissage automatique · Souhaitée
• Amazon Web Services · Souhaitée
• Terraform · Souhaitée
• Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Snowflake
• Databricks
• Apache Spark
• PySpark
• Python
• dbt
• AWS
• Kafka
• SQL
• Machine Learning
• GenAI
• RAG Architectures
• Airflow
• Terraform
• Kubernetes
• MLOps

Domaines d’emploi

• Data & Analytics
• Technology
• Finance & Accounting
• Software
• Consulting