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The role is a contract nCino testing gig focused on a banking SaaS platform built on Salesforce. It's fully remote but the client is based out of Columbia, SC, so you'll be collaborating with a distributed team that services financial institutions. ### What You'll Actually Be Doing You'll spend most of your day writing and executing test cases for nCino modules—loan origination, deposit onboarding, and workflow automation. Expect to dig into configuration settings, validate data mappings between Salesforce and nCino, and work closely with business analysts to reproduce edge‑case scenarios that could break compliance checks. Regression cycles are tight, so you'll also be maintaining a suite of manual and automated tests that run on each release. ### The Core Tech Stack The non‑negotiable skills are deep familiarity with nCino's banking‑specific objects and processes, plus solid hands‑on experience with Salesforce administration (profiles, permission sets, validation rules). You should also understand core banking concepts—loan cycles, KYC, AML—because the test cases are built around those regulations. Knowing how to navigate the Salesforce UI, run SOQL queries, and read audit logs will save you hours each sprint. ### Interview Expectations 1. *How would you validate a loan‑origination workflow in nCino to ensure it meets regulatory compliance?* The interviewer wants to see that you can map business rules to technical test steps, pull relevant fields, and think about edge conditions like missing collateral or credit score thresholds. 2. *Describe your approach to automating regression tests for a custom Salesforce object used in a banking process.* They're probing whether you can pick the right tool (e.g., Selenium, Provar), structure reusable test data, and handle data‑reset between runs without breaking the org’s data integrity. ### Application Advice Tailor your resume to shout out the exact stack: “nCino testing,” “Salesforce admin,” and “banking domain.” Put those phrases in your professional summary and bullet points describing test case design, regression maintenance, and compliance validation. Since it’s a C2C contract, list “Contract – Remote” under employment type and highlight any previous remote or distributed‑team experience. A quick line about “experience with loan‑origination and KYC workflows in nCino” will get past most ATS filters.

The gig is a 6‑month contract as a Blue Yonder WMS techno‑functional consultant, hybrid in Plano, TX. You’ll be the bridge between the warehouse ops team and the Blue Yonder tech stack, making sure the system actually solves real‑world fulfillment problems. ### What You'll Actually Be Doing You’ll spend most of your day digging into Blue Yonder configuration, tweaking pick‑path rules, and translating business‑level requirements into system settings. Expect frequent calls with warehouse supervisors to validate that the WMS reflects floor realities, plus ad‑hoc data‑analysis to spot bottlenecks. When something breaks, you’ll be the first line of defense—debugging integration points, writing work‑arounds, and documenting fixes for the long‑term roadmap. ### The Core Tech Stack The role revolves around Blue Yonder WMS (formerly JDA), so you must be comfortable navigating its modules—receiving, put‑away, picking, and shipping. A solid grasp of SQL for reporting and a working knowledge of REST APIs for third‑party integrations are non‑negotiable. The company also expects you to understand basic warehouse operations, because the “functional” side of the title means you’ll be translating floor‑level pain points into system configurations. ### Interview Expectations 1. *“Walk me through how you would configure a wave release strategy for a high‑mix, low‑volume warehouse in Blue Yonder.”* The interviewer wants to see if you understand both the UI configuration steps and the underlying logic that drives wave generation. 2. *“A shipment is being released but the system is picking the wrong SKU. How do you troubleshoot this within Blue Yonder?”* They’re probing your debugging methodology—checking location mappings, batch rules, and the integration logs—while also gauging how you communicate findings to non‑technical stakeholders. ### Application Advice Tailor your resume to the exact stack: lead with “Blue Yonder WMS” and “Techno‑Functional” in the headline, then list concrete projects where you configured wave releases, managed pick‑path optimization, or built SQL reports for warehouse KPIs. Sprinkle in keywords like “Hybrid”, “Plano”, “integration”, “REST API”, and “warehouse operations” so the ATS sees a direct match. A short cover note that mentions the 6‑month timeline and your comfort with hybrid onsite/remote work will help you stand out.

This role is a senior‑level WMS implementation consultant focused on Manhattan Active, based out of Seattle. You’ll be the go‑to person for end‑to‑end deployments, bridging warehouse hardware, TMS and integration teams. ### What You'll Actually Be Doing You’ll own the full lifecycle of Manhattan Active roll‑outs: run design workshops, write detailed design docs, configure the system, and build custom extensions when the out‑of‑the‑box features fall short. Day‑to‑day you’ll be juggling host‑system interfaces, appointment scheduling, MHE/WCS integration, and TMS coordination, making sure data flows cleanly across all moving parts while keeping the project timeline on track. ### The Core Tech Stack The non‑negotiables are deep knowledge of Manhattan Active WMS (configuration, master data, and extension development) and hands‑on experience creating proactive extensions. You also need to be comfortable with warehouse hardware (MHE, WCS) and TMS APIs, because the job is as much about wiring systems together as it is about the WMS itself. Strong design‑session facilitation and documentation skills are critical to keep every stakeholder aligned. ### Interview Expectations * Expect a scenario where you’re asked to design a custom extension to handle a non‑standard inbound workflow. The interviewer will probe how you’d use proactive extensions, what data model changes you’d make, and how you’d test integration with the host system – they want to see you can translate a business need into clean, maintainable code. * Another likely question will dive into a past end‑to‑end implementation you led: they’ll ask for specifics on how you coordinated between the MHE team, the TMS group, and the interface developers, looking for evidence you can manage cross‑functional dependencies and resolve conflicts under pressure. ### Application Advice Tailor your résumé to highlight “Manhattan Active” and “proactive extensions” right up front, and sprinkle in phrases like “design sessions,” “end‑to‑end implementation,” and “integration with MHE/WCS/TMS.” Quantify your experience (e.g., “12+ years delivering WMS projects, 2 full lifecycle Manhattan Active deployments”) and list the exact tools you used (e.g., “Manhattan Active Configurator, SQL, REST APIs”). Those keywords will get you past most ATS filters and signal you’ve done exactly what this role demands.

They need a Java full‑stack developer to keep their microservices and Angular front‑end humming. The role is on‑site in Virginia and only open to OPT candidates. ### What You'll Actually Be Doing You’ll be writing Spring Boot services, exposing REST APIs, and wiring them up to an Angular UI that the product team owns. Expect daily debugging of distributed calls, writing unit and integration tests, and pushing code through a CI/CD pipeline. Because the stack is full‑stack, you’ll also be tweaking UI components, handling state management, and ensuring the front‑end talks cleanly to the back‑end while meeting performance SLAs. ### The Core Tech Stack The non‑negotiables are Java 8+ with Spring Boot for the service layer and a microservices architecture—think Docker, Kubernetes, and circuit‑breaker patterns. On the client side you must be comfortable with Angular (components, services, RxJS) because the UI is a single‑page app that consumes those APIs. They also expect you to know Git, RESTful design, and basic CI/CD tooling; without those you’ll hit friction fast. ### Interview Expectations 1. *Design a resilient microservice*: They’ll ask you to sketch how you’d build a service that can survive downstream failures—expect discussion of circuit breakers, retries, fallback strategies, and health‑check endpoints. They’re watching for whether you understand latency, fault isolation, and observability. 2. *Angular change detection performance*: Be ready to explain how Angular’s change detection works, when you’d use OnPush strategy, and how to avoid unnecessary re‑renders. The interviewer wants proof you can keep the UI snappy at scale. ### Application Advice Tailor your resume to echo the exact stack: lead with “Java, Spring Boot, Microservices, Angular” and quantify the years (3+). Mention any experience with Docker/Kubernetes, CI/CD pipelines, and REST API design—these keywords will get past the ATS. Since the posting is OPT‑only and local, flag your work‑authorization status and Virginia residency prominently near the top of your CV.

This is a senior‑level Data & AI Solution Architect role focused on building end‑to‑end analytics platforms for finance teams, with a heavy emphasis on Databricks and cloud services. You’ll be in a hybrid office split between Boston or Jersey City, so you need to be comfortable showing up three days a week. ### What You'll Actually Be Doing You’ll design and ship data pipelines that pull raw transactional feeds into a Lakehouse on Databricks, then expose curated tables to downstream reporting and ML models. Expect to spend a good chunk of time translating finance‑centric use cases (risk, forecasting, regulatory reporting) into scalable Spark jobs, while also shepherding data governance, security, and cost‑optimization policies across AWS or Azure. Collaboration is key – you’ll be the bridge between data engineers, product owners, and finance analysts, constantly iterating on schema designs and performance tweaks. ### The Core Tech Stack The non‑negotiables are Databricks (Spark SQL, Delta Lake), a major cloud provider (AWS or Azure), and strong data‑modeling chops in relational/OLAP contexts. You’ll also need to be fluent in Python or Scala for pipeline code, and comfortable with CI/CD tooling (Terraform, GitLab CI) to keep the infrastructure reproducible. The finance angle means you’ll be dealing with strict compliance (e.g., SOX) and need a solid grasp of data lineage and masking techniques. ### Interview Expectations 1. *“How would you optimise a Spark job that’s spilling to disk when processing a 10 TB finance dataset?”* – They want to see your understanding of partitioning, caching, broadcast joins, and cluster sizing, plus cost‑aware tuning. 2. *“Walk me through designing a data‑governance framework for a multi‑region Databricks deployment handling PII.”* – Expect them to probe your knowledge of Unity Catalog, role‑based access, encryption at rest, and audit logging. ### Application Advice Tailor your resume to shout out the exact buzzwords the posting uses: “Data & AI Solution Architect”, “Databricks”, “Hybrid”, “Finance”, “AWS/Azure”, “Spark”, “Delta Lake”, “SOX compliance”, and “CI/CD”. Highlight any end‑to‑end projects where you built a Lakehouse for finance or risk analytics, and quantify impact (e.g., reduced reporting latency by 40%). A short cover note that mentions the Boston/Jersey City hybrid model and your willingness to be onsite three days will help you pass the ATS and catch the recruiter’s eye.

The posting is a bare‑bones call for a Mainframe Developer to work onsite in Phoenix. No fluff about perks or culture—just a need for someone who can keep legacy systems humming. ### What You'll Actually Be Doing You’ll spend most of your day in a COBOL‑heavy environment, maintaining and enhancing batch jobs, troubleshooting JCL failures, and supporting production runs. Expect tight SLA pressure when a nightly run blows up, and you’ll be the go‑to person for root‑cause analysis and quick fixes. Collaboration will be with a small ops team that still relies on green‑screen interfaces, so clear communication about changes is key. ### The Core Tech Stack The role revolves around COBOL, JCL, and mainframe DB2/IMS back‑ends. You’ll also need a working knowledge of CICS or IMS transaction processing because the applications you touch are likely tied into those middleware layers. The company wants someone who can read and modify legacy code without breaking downstream processes, so deep debugging skills in a TSO/ISPF environment are non‑negotiable. ### Interview Expectations 1. *“Walk me through how you would diagnose a failing batch job that exits with a S0C7 dump.”* – They’re looking for your step‑by‑step approach: dump analysis, ABEND code interpretation, checking data set integrity, and using tools like IKJEFT01 or FDREPORT. 2. *“Explain the difference between VSAM KSDS and ESDS and when you’d choose one over the other.”* – This tests whether you understand storage organization and performance trade‑offs, a must for any mainframe performance tuning. ### Application Advice Tailor your resume to surface the exact buzzwords the JD hints at: **COBOL**, **JCL**, **CICS/IMS**, **DB2**, **VSAM**, **TSO/ISPF**, and **batch processing**. Highlight any production support incidents you resolved under pressure, and quantify the impact (e.g., “Reduced nightly job failures by 30%”). Use the phrase “onsite Phoenix” to show you’re aware of the location requirement and can work on‑site.

AI Developer role based in Denver, on‑site from day one. The team is building generative AI services that power internal tools, so you’ll be deep in model integration, prompt engineering, and production‑grade MLOps. If you’ve spent a decade building Python back‑ends and wrestling with vector stores, this is a straight‑shoot gig. ### What You'll Actually Be Doing You’ll own end‑to‑end pipelines: design FastAPI endpoints, stitch together LLMs (OpenAI, Claude, Llama) with Retrieval‑Augmented Generation, and keep the whole thing running on CI/CD pipelines using MLflow. Expect daily debugging of latency spikes in vector DBs like Pinecone/FAISS, writing SQL queries for reporting, and collaborating with product folks to translate vague AI use‑cases into concrete APIs. ### The Core Tech Stack The stack is Python‑first: heavy use of PyTorch or TensorFlow for model work, Scikit‑learn for classic ML, and FastAPI for the HTTP layer. You’ll need to be comfortable with TypeScript when the front‑end team asks for typed contracts. The real kicker is RAG architecture—so you must know how to embed documents, manage vector stores, and tune retrieval pipelines. MLOps isn’t an afterthought; you’ll be wiring MLflow into GitHub Actions or similar CI tools to automate model versioning and deployment. ### Interview Expectations 1. *Design a RAG pipeline that can answer questions over a 10 GB knowledge base with sub‑second latency.* The interviewer will look for your approach to chunking, embedding model choice, vector store indexing strategy, and how you’d cache results in production. 2. *Explain how you would containerize a PyTorch model and set up a rolling update without dropping in‑flight requests.* They want to see your grasp of Docker, orchestrators (K8s or ECS), and zero‑downtime deployment patterns, plus how you’d monitor drift with MLflow. ### Application Advice Tailor your resume to hit every must‑have keyword: Python, AI/ML, Generative AI, TypeScript, FastAPI, OpenAI/Claude/Llama, PyTorch/TensorFlow, Scikit‑learn, RAG, Pinecone/FAISS, MLOps, MLflow, CI/CD, REST APIs, and SQL. Highlight any on‑site or hybrid experience in Denver to reassure they can slot you in immediately. If you have visa sponsorship experience, surface it early— the posting explicitly asks for visa type.

This role is a senior‑level AWS data platform specialist based in St. Louis, focusing on building end‑to‑end data pipelines on the AWS stack. If you enjoy turning raw logs into reliable analytics and have a telecom background, this could be a good fit. ### What You'll Actually Be Doing You’ll spend most of your day stitching together S3 buckets, Glue jobs, and Redshift clusters to move terabytes of data from source to analytics. Expect to design both batch and ELT pipelines, write performant SQL for data modeling, and use Lambda + CloudWatch for orchestration and monitoring. The job will also require you to troubleshoot schema drift and keep the pipelines cost‑effective. ### The Core Tech Stack The non‑negotiables are deep experience with AWS S3, Glue, Redshift, Lambda, and CloudWatch, plus solid SQL development and data‑modeling chops. The company needs you to own the whole data flow—from raw ingestion in S3, through transformation in Glue, to loading and serving in Redshift—so any gaps in those services will quickly become blockers. ### Interview Expectations 1. *Design a near‑real‑time pipeline*: Explain how you’d ingest JSON files from S3, transform them with Glue, and load into Redshift while handling schema evolution and ensuring low latency. The interviewer is looking for your ability to balance consistency, performance, and cost, plus how you’d use versioned schemas or Glue job bookmarks. 2. *Lambda‑CloudWatch error handling*: Describe a situation where you used Lambda functions triggered by CloudWatch events to catch ETL failures, what metrics you’d surface (e.g., duration, error count, DLQ size), and how you’d set up alerts. They want to see you can build resilient, observable pipelines, not just fire‑and‑forget jobs. ### Application Advice Tailor your resume to hit the exact buzzwords the JD repeats: **AWS S3, AWS Glue, AWS Redshift, ETL/ELT pipelines, data modeling, SQL development, AWS Lambda, CloudWatch**, and if you have any telecom domain projects, surface them front‑and‑center. Highlight 8‑10 years of end‑to‑end data platform work and quantify impact (e.g., “Reduced pipeline latency by 30%”). Use the phrase “Face‑to‑Face interview” only if you’re comfortable with onsite work, as the posting explicitly mentions it.