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• Design, implement, test, and release features end-to-end with limited guidance. • Independently identify edge cases and potential failure modes within assigned work. • Write clean, maintainable code and provide constructive, specific feedback during code reviews. • Proactively communicate blockers or ambiguities to the team. • Apply practical knowledge of federated identity standards including OAuth 2.0, OIDC, SAML 2.0, and JWT. • Understand and reason about authorization frameworks, policy enforcement, and integration into upstream and downstream services. • Reason clearly about token lifecycle, session management, and the security implications of identity design choices. • Understand the mechanics of connecting distributed services in a multi-product platform environment. • Build and maintain integrations that are observable, fault-tolerant, and respectful of downstream consumers. • Write and work with API contracts (REST, event-driven patterns) with attention to versioning and backwards compatibility. • Actively use AI-assisted development tools (e.g., Claude Code, Codex) to accelerate design, implementation, and validation. • Participate in building and improving AI-assisted workflows and evaluation harnesses. • Stay current on evolving AI tooling and introduce new approaches to the team. • Write tests with sufficient coverage to catch regressions without over-engineering. • Contribute to operational health by monitoring shipped features, responding to issues, and following incidents to resolution. • Document systems and processes to reduce reliance on tribal knowledge. • Build and extend platform services for federated identity, authentication, and authorization. • Implement integrations with the centralized identity platform, including token handling and session management. • Support product teams with integration processes, debugging, and navigating edge cases. • Develop tooling, documentation, and examples for self-service integrations. • Design and implement backend service integrations using Python and common patterns. • Build with multi-product and multi-tenant scenarios in mind. • Participate in API design reviews. • Use AI development tools as an active part of the delivery workflow. • Contribute to AI harness development to improve team velocity. • Share effective AI-assisted engineering patterns with the team. • Write unit, integration, and acceptance tests. • Instrument services for observability (logging, metrics, alerting). • Participate in on-call rotations and incident response. • Participate in design discussions and code reviews, giving and receiving feedback constructively. • Provide mentorship and review support to junior engineers. • Contribute to internal documentation, runbooks, and onboarding materials.

• Define the enterprise data architecture: Own the conceptual, logical, and physical data models for MeridianLink's analytical and operational data platform, including source-aligned, integrated, and consumption-ready layers. • Build the meta-model: Design and maintain a meta-model that captures entities, relationships, business definitions, ownership, lineage, sensitivity classifications, and SLAs — and make sure it is wired into our tooling, not stuck in a slide deck. • Drive the lakehouse strategy: Architect our medallion (bronze / silver / gold) Delta Lake patterns on Databricks; define standards for partitioning, clustering, schema evolution, slowly changing dimensions, and historical reproducibility. • Be hands-on: Write PySpark, SQL, and Delta Lake code. Build reference implementations, prototype patterns, review pull requests, and personally model critical domains rather than delegating every detail. • Lead data integration design: Set patterns for ingestion through Informatica Data Management Cloud (IDMC) and direct Databricks pipelines, including CDC, batch, streaming, and API-based sourcing from our SaaS products and third-party systems. • Champion data governance and lineage: Partner with data governance, security, and compliance leaders to operationalize cataloging, lineage, classification, masking, and access controls across the platform (Unity Catalog, IDMC, and adjacent tools). • Standardize data modeling practices: Establish the standards, naming conventions, and review processes used by the Data Engineering team. Coach engineers on dimensional modeling, Data Vault, and other techniques where they best fit the use case. • Partner across the business: Work closely with Product, Engineering, Analytics, ML, Finance, Risk, and Customer-facing teams to translate business needs into durable data designs. • Influence the roadmap: Identify gaps in tooling, capability, and skill; propose investments; and drive multi-quarter initiatives that materially improve how MeridianLink uses its data.

• Design, develop, and maintain scalable full-stack applications across both front-end and back-end systems. • Build and maintain robust microservices using Java Spring Boot. • Develop front-end applications using Angular 19 • Design and implement distributed, scalable solutions aligned with business requirements. • Design and manage databases using PostgreSQL and ensure efficient data integration across services. • Containerize applications using Docker and manage deployments with Kubernetes (K8S). • Collaborate closely with designers, product managers, and cross-functional teams to deliver high-quality products. • Participate in requirement analysis, solution design, and technical decision-making across the stack. • Ensure code quality through best practices, including testing (unit/integration), code reviews, CI/CD, and documentation. • Work effectively in global teams and contribute to Agile/Scrum development processes.

• Develop and maintain robust, high-performing microservices using Java Spring Boot. • Design and implement scalable and distributed solutions to meet complex business requirements. • Utilize PostgreSQL for database management and integration within services. • Create and manage containerized applications using Docker and orchestrate deployments using Kubernetes (K8S). • Work collaboratively with stakeholders to analyze requirements, propose solutions, and ensure alignment with business goals. • Develop comprehensive testing strategies, including unit and integration testing, to ensure reliable and secure applications. • Ensure code quality and maintainability by following best practices and conducting regular code reviews.

• Own end-to-end project execution: plan, schedule, and drive delivery across the full project lifecycle, balancing constraints and keeping work moving forward in partnership with product and engineering counterparts. • Build and maintain project plans, track milestones, and proactively manage risks, issues, and tradeoffs—providing clear stakeholder updates with recommended solutions. • Serve as the PMO lead for the ML's agentic platform and customer facing products — the structured methodology for developing, testing, deploying, and operating AI agents within MeridianLink’s agentic architecture. • Partner with engineering leads to embed AI into each SDLC phase: requirements (AI-assisted grooming agents), development (co-pilot and Claude Code standards), testing (test-driven development metrics), and release (zero-downtime deployment, feature flag governance). • Establish and continuously improve team processes that increase quality and productivity through process definition, education, and refinement. • Facilitate light weight agile ceremonies (planning, standups, retrospectives) and drive continuous improvement through retrospective assessments. • Use tools such as Jira (and related reporting) to support teams and enable visibility into progress, capacity, and delivery health. Bridge product and engineering AI alignment.

• Architect and implement end-to-end data solutions using Databricks Lakehouse Platform • Design and develop scalable data pipelines using PySpark, Spark, and SQL • Lead cloud data platform implementations on AWS/Azure/GCP • Drive modernization of legacy data systems to cloud-native architectures • Implement Medallion Architecture (Bronze, Silver, Gold layers) • Ensure data governance, security, and compliance (especially for Financial Services) • Collaborate with business stakeholders, data scientists, and engineering teams • Optimize performance, cost, and reliability of data pipelines • Mentor and guide data engineering teams and provide architectural leadership.

• Act as the primary point of contact for platform-related questions, support, and ongoing enhancements • Configure and maintain workflows, transaction stages, and document templates to align with business needs and regulatory requirements • Partner with internal teams to optimize processes, streamline operations, and improve user adoption • Support end users by troubleshooting issues, delivering training, and creating clear documentation (guides, runbooks, and resources) • Oversee user roles, permissions, and data security to ensure sensitive information is protected • Collaborate with cross-functional teams to identify system limitations and recommend scalable solutions

• Provide technical leadership in the design, integration, implementation, and transition of enterprise SaaS human services system • Flexible design architectural approach to deliver configurability along the data creation, integrations, workflows, notifications, and data persistence • Understand and synthesize integration requirements, and develop recommendations based on business objectives, product roadmap, solution architecture and technical considerations • Contribute to the development of our platform functionality using state-of-the-art frameworks and tools Specify and troubleshoot API integrations in an ecosystem of multiple systems • Work closely with client stakeholders, partners, product managers, creative designers, platform architect, and other software engineers • Constantly learn and use leading-edge technologies • Implement a system that will address the needs of vulnerable populations • Contribute to the highest security, extensibility, reusability and testing standards in system architecture and software, interface, components, data structures, and algorithms specifications.

• Design and implement robust, scalable data pipelines for structured and unstructured data. • Oversee ETL/ELT processes to ingest data from core banking systems, CRM, and external sources. • Ensure data models support real-time analytics, reporting, and machine learning. • Collaborate with data scientists and analysts to provide usable, cleaned and aggregated data to end users • Ensure data practices comply with banking regulations (e.g., Basel III, GDPR, local laws). • Implement data governance frameworks, including metadata management and lineage tracking. • Collaborate with cybersecurity teams to safeguard sensitive financial data. • Monitor and optimize data infrastructure for performance, reliability, and cost-efficiency. • Troubleshoot data issues and ensure high availability of data services. • Evaluate and adopt modern data technologies (e.g., cloud platforms, data lakes, streaming tools). • Promote automation and DevOps practices in data engineering workflows.

• Manage and maintain SCCM/MECM environment, including software deployment, patching, and configuration management • Create, package, test, and deploy applications across enterprise environments • Support operating system deployments (OSD) and image management • Perform ongoing patch management, including 3rd party patching solutions (e.g., Recast or similar) • Assist with Intune / Endpoint Manager for device management and co-management scenarios • Monitor SCCM performance, troubleshoot deployment issues, and ensure system reliability • Maintain documentation for deployment processes and configurations • Collaborate with cross-functional IT teams to support endpoint lifecycle management