Risk Data Engineer/Leads

Job Description

Risk Data Engineer/Leads Job Description: - Our Technology team builds innovative digital solutions rapidly and at scale to deliver the next generation of Financial and Non- Financial services across the globe. The Position is a senior technical, hands-on delivery role, requiring the knowledge of data engineering, cloud infrastructure and platform engineering, platform operations and production support using ground-breaking cloud and big data technologies. The ideal candidate with 3-6 years of experience, will possess strong technical skills, an eagerness to learn, a keen interest on 3 keys pillars that our team support i.e. Financial Crime, Financial Risk and Compliance technology transformation, the ability to work collaboratively in fast-paced environment, and an aptitude for picking up new tools and techniques on the job, building on existing skillsets as a foundation. In this role you will: Develop a thorough understanding of the data science lifecycle, including data exploration, preprocessing, modelling, validation, and deployment Design, build, and maintain tree-based predictive models, such as decision trees, random forests, and gradient-boosted trees, with a low-level understanding of their algorithms and functioning. Ingestion and provisioning of raw datasets, enriched tables, and/or curated, re-usable data assets to enable variety of use cases. Evaluate modern technologies, frameworks, and tools in the data engineering space to drive innovation and improve data processing capabilities. Core/Must Have skills. Significant data analysis experience using python, SQL and spark. Experience in python or spark to write scripts for data transformation, integration and automation tasks. 3-6 years experience with cloud ML (AWS) or any similar tools. Design, build, and maintain tree-based predictive models, such as decision trees, random forests, and gradient-boosted trees, with a low-level understanding of their algorithms and functioning. Strong Experience with statistical analytical techniques, data mining and predictive models. Conduct A/B testing and other model validation techniques to ensure the accuracy and reliability of data models.. Experience with optimization modelling, machine learning, forecasting and/or natural language processing. Hands-on experience with Amazon S3 data storage, data lifecycle policies, and integration with other AWS services. Maintain, optimize, and scale AWS Redshift clusters to ensure efficient data storage, retrieval, and query performance Utilize Amazon S3 to store raw data, manage large datasets, and integrate with other AWS services to ensure secure, scalable, and cost-effective data solutions. Experience in implementing CI/CD Pipelines in AWS. At least 4+ years of experience in Database Design and Dimension modelling using SQL Advanced working SQL Knowledge and experience working with relational and NoSQL databases as well as working familiarity with a variety of databases (SQL Server, Neo4J) Strong analytical and critical thinking skills, with ability to identify and resolve issues in data pipelines and systems. Strong communication skills to effectively collaborate with team members and present findings to stakeholders. Collaborate with cross-functional teams to ensure successful implementation of solutions. Experience with OLAP, OLTP databases, and data structuring/modelling with understanding of key data points. Good to have: Apply domain knowledge (if applicable) in financial fraud to enhance predictive modelling and anomaly detection capabilities. Knowledge of AWS IAM for managing secure access to data resources. Familiarity with DevOps practices and automation tools like Terraform or CloudFormation. Experience with data visualization tools like Quick Sight or integrating Redshift data with BI tools (Tableau, PowerBI, etc.). AWS certifications such as AWS Certified Data Analytics Specialty or AWS Certified Solutions Architect are a plus.