Role Description - Backend: Java 11/17+ (Spring Boot 3.x, Spring Batch, Spring Cloud Gateway, JPA/Hibernate); Scala (Akka, Play); Groovy (Gradle, Spock) - Frontend: Angular (TypeScript, RxJS, NgRx); RESTful API integration for bank/supplier portals - Data & Messaging: Apache Spark (batch invoice processing); Kafka/RabbitMQ/ActiveMQ; Elasticsearch - Containers: Docker, Kubernetes (Helm awareness), Terraform IaC review - Reporting & Integration: Jasper Reports; Talend ETL for bank/ERP integrations - Testing: JUnit 5, Mockito, Spock, REST-assured, SonarQube - Database: PostgreSQL, Oracle, or MongoDB; SQL optimisation; Flyway/Liquibase - Develop, test, and deploy microservices for SCF modules: invoice ingestion, deal config, funder allocation, settlement, dynamic discounting. - Build and maintain Spark batch/streaming jobs for invoice reconciliation and portfolio analytics. - Integrate bank payment gateways and ERP connectors via Talend/REST APIs with idempotent, auditable flows. - Build Angular portal features for buyer approval, supplier self-service, and funder dashboards. - Write Jasper report templates for funder statements, utilisation reports, and regulatory disclosures. - Participate in code reviews, sprint ceremonies, and technical design discussions. - Maintain Kubernetes manifests; troubleshoot issues via Elasticsearch and Prometheus/Grafana. Qualifications - 3–7 yrs software development (Mid: 3–5 yrs; Senior: 5–7 yrs). - Solid Java (Spring Boot); Scala or Python a strong plus. - Production experience with Kafka, RabbitMQ, or MQ. - Working Docker and Kubernetes deployment knowledge. - Financial services, payments, or trade finance experience preferred. - Nice to have: Scala (Akka, Play); Groovy (Gradle, Spock) Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

• Querying complex source systems in a range of health data sources (e.g., EMRs, ECG data, DICOM data) to identify and map data elements in order to create high-quality datasets to support training AI algorithms and conducting in-depth analyses of patient journeys • Developing and maintaining data transformation pipelines using dbt and Snowflake, ensuring data quality, lineage, and transparency. • Harmonizing multimodal data from multiple health systems into a unified ontological layer ready for use by data scientists and AI developers, with input from clinical experts across the company. • Performing complex data extraction, manipulation, and summarization of large databases to create analytical data models; • Implementing data quality tests, CI/CD and version control best practices for analytics codebases throughout the data modeling pipeline. • Supporting software engineers in optimizing processes and technical solutions for de-identification and ETL of clinical data from disparate health system cloud environments to Dandelion Health’s data warehouse. • Working with experts in natural language processing (NLP) to structure and model discrete clinical concepts data that have been abstracted from unstructured and semi-structured text data. • Collaborating with technical and clinical subject matter experts and Dandelion customers to translate research and model requirements into engineering solutions.

• Own implementation and on-going operation of recurring analytics pipelines (e.g., Airflow DAGs) including monitoring, alerting, and reliability improvements • Lead architectural evolution of the analytics platform, including schema standardization, DAG consolidation, and modernization of legacy workflows • Drive cross-team technical alignment when consolidating duplicated or inconsistent analytics outputs • Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation • Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines • Improve transparency and usability through documentation, discoverability, and clear data contracts • Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage • Mentor engineers through design reviews and raise the bar for operational and modeling rigor

• Own the analytics platform roadmap — prioritizing improvements, architecture decisions, and tooling enhancements in partnership with IT and the Head of Data, Analytics and AI Solutions • Define and enforce platform SLAs, data quality standards, and operational runbooks • Serve as technical mentor to the Analytics Engineer/Data Scientist • Own the end-to-end data pipeline from ingestion through transformation and delivery into BI tools and data products across our AWS stack (Step Functions, Glue, Snowflake, dbt, Sigma) • Partner with third-party vendors to maintain DataOps infrastructure using infrastructure-as-code practices • Monitor pipeline health and drive timely resolution of data issues with providers and consumers • Maintain and evolve an enterprise data model, data dictionary, and business rules agnostic across sources, insurance products, and distributors • Enforce Snowflake security best practices — masking policies, RBAC, and least-privilege access — aligned to GLBA NPI/NPI-H classification requirements • Champion data governance, lineage, and quality standards; support NAIC MDL-668 audit trails, SOC 2 Type II evidence collection, and adherence to CCPA/CPRA, HIPAA, and state breach notification requirements • Design and maintain systems of record for core insurance domains (policy, claims, billing, distribution, customer) with proper versioning, effective dating, and audit history • Own database design and management for AI tools and models, including vector databases, feature stores, and model input/output pipelines • Design and maintain a semantic layer enabling natural language insight discovery • Own executive and operational dashboards from board-level reporting down to day-to-day operations • Set BI development standards for the Analytics Engineer/Data Scientist to execute against • Maintain and enhance actuarial data pipelines including distributed compute and Glue Jobs for data curation • Advise on data architecture decisions supporting future AI/ML and modeling use cases