Cleint currently seeks a "Senior Data Practitioner/Data Lead" to join our team in "New York, NY". [Hybrid 3 days/week to onsite is must]
Location: New York City, NY (Hybrid)
This role requires close collaboration with business stakeholders in New York and global technology teams in India.
Interview Requirement:
Candidates must be available for in-person interviews and periodic onsite collaboration at the Midtown Manhattan office.
We are seeking a highly experienced Senior Data Practitioner / Data Lead to drive enterprise-wide data management initiatives across data acquisition, provisioning, governance, and analytics. This role will act as the lead for the data function, partnering closely with business stakeholders in New York and global technology teams in India to deliver scalable, secure, and high-quality data solutions.
The ideal candidate will bring strong expertise in Financial Crime, Fraud Analytics, AML, and Banking data ecosystems, with a proven track record of building and governing real-time data platforms while enabling business insights through modern data engineering practices and AI-driven automation
Day-to-Day Job Duties:
- Lead end-to-end data management initiatives across data acquisition, data provisioning, data governance, data quality, metadata management, and analytics enablement.
- Design, develop, and maintain scalable data platforms that support Fraud Technology, Financial Crime, AML, and Non-Financial Risk business functions.
- Build and manage high-volume batch and real-time data pipelines using modern distributed data technologies.
- Develop data ingestion, transformation, enrichment, and provisioning frameworks to support operational, analytical, and machine learning workloads.
- Enable real-time data persistence and delivery to fraud investigation, surveillance, and risk management applications.
- Design and maintain enterprise data lake environments, ensuring data is curated, governed, secure, and analytics ready.
- Partner with Data Scientists and AI teams to support feature engineering, model training, model inference, and deployment of fraud detection and risk scoring solutions.
- Utilize PyTorch to support machine learning workflows, fraud analytics models, anomaly detection frameworks, and AI-enabled automation initiatives.
- Build and optimize data pipelines that feed AI/ML models and support model monitoring, retraining, and operationalization.
- Implement enterprise data governance standards covering data quality, cataloging, lineage, metadata management, security, privacy, and regulatory compliance.
- Develop automated frameworks and dashboards for monitoring Data Quality KPIs, data integrity, and platform performance.
- Collaborate with business stakeholders in New York and global technology teams in India to deliver strategic data solutions.
- Drive automation of data engineering and data management processes using AI, machine learning, and advanced analytics techniques.
- Participate in architecture reviews, code reviews, Agile ceremonies, production support, and platform modernization initiatives.
- Mentor junior engineers and promote adoption of modern data engineering, AI, and MLOps best practices.
Basic Qualifications:
- Minimum 8+ years of experience in Data Engineering, Data Management, Data Architecture, or Data Governance.
- Minimum 5+ years of experience leading enterprise-scale data initiatives and delivering complex data solutions.
- Strong experience within Banking, Financial Services, Capital Markets, or Financial Institutions.
- Proven experience supporting Fraud Analytics, Financial Crime, AML, KYC, Risk Management, or Regulatory Reporting functions.
- Strong expertise in data architecture, data modeling, and enterprise data management practices.
- Extensive hands-on experience developing enterprise data solutions using Python and SQL.
- Strong experience with machine learning and AI-enabled data platforms.
- Hands-on experience with PyTorch for machine learning model development, feature engineering, training, inference, and deployment support.
- Experience building and maintaining data pipelines that support fraud detection, predictive analytics, anomaly detection, and AI-driven decision-making.
- Strong understanding of MLOps concepts, model lifecycle management, and integration of machine learning models within enterprise applications.
- Experience designing and supporting real-time and near real-time data architecture.
Strong hands-on experience with:
- Apache Spark
- Hadoop Ecosystem
- SQL Server
- Distributed Data Platforms
- Enterprise Data Lakes
- Experience implementing enterprise data governance practices including:
- Data Quality
- Data Cataloging
- Metadata Management
- Data Lineage
- Data Security & Compliance
- Experience working in Linux environments and developing Python/Shell scripts.
- Experience with Agile development methodologies, CI/CD pipelines, source control systems, and DevOps practices.
- Strong analytical, problem-solving, communication, and stakeholder management skills.
- Ability to work effectively across geographically distributed teams and business functions.
Required Technology Experience:
- Python
- PyTorch
- Apache Spark
- Hadoop
- SQL Server
- SingleStore (Distributed SQL Database)
- Kubernetes
- Python-based Microservices
- REST APIs
- Graph APIs
- Enterprise Data Lakes
- Data Governance Frameworks
- Data Quality Platforms
- Linux
- Shell Scripting
- Autosys, Control-M, or equivalent scheduling tools
- Git, CI/CD, and DevOps Toolchains
Preferred Qualifications:
Experience leveraging PyTorch for fraud detection, graph analytics, anomaly detection, predictive risk scoring, and AI-driven surveillance solutions.
Experience developing and deploying deep learning models in production environments.
Experience with MLOps platforms and machine learning deployment frameworks.
Experience with Graph-based analytics using Neo4j.
Experience processing real-time business events using Kafka or similar streaming platforms.
Experience building near real-time feature engineering pipelines for machine learning inference.
Strong knowledge of Azure data platforms including:
- Azure Databricks
- Azure Data Factory
- Azure Synapse Analytics
- Azure Data Lake Storage
- Experience with AWS data services including:
- Amazon S3
- AWS Athena
- AWS Glue
Experience with Snowflake Data Cloud.
Experience implementing AI and Generative AI solutions to automate data engineering and operational processes.
Experience supporting enterprise Fraud Prevention, Fraud Investigation, Financial Crime Monitoring, or Risk Analytics programs.
Knowledge of modern data architecture patterns including Data Mesh and Data Fabric.
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