Enterprise Data and Analytics

SCOPE OF THE ROLE You will be responsible for end-to-end delivery of enterprise data and analytics solutions leveraging traditional and modern Data architecture. Experience with Financial Crime Analytics, Finance & Credit Risk Analytics, Credit Scoring & Decision systems, Retail & Wholesale Datamarts will of advantage The role spans requirements analysis, solution design, testing, implementation, and production support ensuring high-quality, scalable, and compliant data platforms that support advanced analytics and AI initiatives. Key Responsibilities • Lead end-to-end solution delivery for data and analytics across the full SDLC. • Analyze business and regulatory requirements, translate them into scalable solution designs & provide estimations. • Communicate complex technical and architectural concepts to business and senior stakeholders in a clear, simplified manner. • Review and approve test strategies, functional test cases, and data validation approaches. • Manage risks and issues related to scope, data quality, regulatory commitments, and delivery timelines. • Participate in product and platform evaluations (RFPs, PoCs) for data, analytics, and AI tooling. • Partner with production support team to conduct root cause analysis, resolution, and preventive controls. • Lead innovation and modernization initiatives, including data discovery, cataloguing, governance, and AI enablement. • Drive productivity, efficiency & quality improvements across delivery and operational processes. • Ability to design data architectures supporting NLP and AI-driven analytics. FUNCTIONAL SKILLSETS Analytics Domains • Financial Crime Analytics Transaction Monitoring, Customer Due Diligence, Sanctions & Payments Screening • Finance & Credit Risk Analytics Financial reconciliation, Allocation, Performance management, Regulatory and Management reporting, Credit risk exposure, NPL, Counterparty risk, Basel & IFRS9 input variables Enterprise Data, Analytics & Unstructured Data Enablement Proven experience delivering large-scale analytics platforms within financial services spanning structured, semi structured, and unstructured data • Strong capability in requirements analysis and functional design for analytics use cases involving Transactional data, Investigator narratives, Case notes and alerts, Policy & Customer communications documents • Experience defining data quality, governance, lineage, and reconciliation controls for both structured and NLP-derived datasets. Unstructured Data & NLP-Enabled Analytics • Ability to define data architectures and data flows that ingest, curate, and govern unstructured and semi-structured data within enterprise data platforms. • Experience translating business requirements into NLP-enabled analytical use cases, such as Text classification and categorization, Entity & relationship extraction, Risk indicator identification, Summarization of alerts, cases, or documents Knowledge Graph & Relationship‑Based Analytics • Ability to design and govern an enterprise knowledge layer defining relationship taxonomies, entity resolution rules, and linkage logic • Ability to translate use cases into relationship‑driven analytical designs, such as Network‑based risk identification, Hidden association and indirect exposure analysis, Related‑party and concentric risk detection TECHNICAL SKILLSETS Certifications At least two relevant technical certifications across data platforms, cloud, or analytics technologies. Data Platforms & Architecture • Open table formats: Apache Iceberg, Delta Lake, Apache Hudi • Distributed processing & query engines: Spark, Trino/Presto, Hive • Cost optimization strategies: tiered storage, lifecycle management, workload governance Programming & Analytics • SQL, BTEQ, GCFR • Python (Pandas, NumPy) • BI & visualization tools: Power BI, QlikSense Data Integration & Quality • Informatica suite: PowerCenter, BDM, IDQ, Enterprise Data Catalogue • Data ingestion patterns: batch, CDC, streaming • Data validation, quality controls, and reconciliation frameworks within environments Governance, Risk & Compliance • Data modelling, critical data elements, regulatory reporting • Fine-grained data access controls (row-level, column-level, masking) • Metadata management, lineage, and impact analysis • Compliance with BCBS 239, MAS, AML/CFT, and internal data standards Big Data Platforms • Cloudera Hadoop distribution: Hive, Impala, Spark, Iceberg, Trino