Jobs at AllCloud

Drive revenue for Salesforce implementation services by building pipelines, managing full sales cycles, creating SOWs, and cultivating C-suite relationships. Collaborate with Salesforce account teams, lead cross-functional internal resources, negotiate large contracts, and achieve quota while supporting strategic enterprise transformations.

Lead and deepen global partner relationships (primarily AWS and Salesforce). Open new partner doors, build high-trust technical and business relationships, map partner ecosystems, align internal teams for co-marketing and delivery, manage partner operations, and drive sustained inbound opportunities across regions.

Design and implement scalable Salesforce solutions, translate business requirements into architecture, lead planning/estimation, mentor developers, integrate with other systems, resolve technical issues, and keep solutions aligned with Salesforce best practices.

Own the technical presales lifecycle for AllCloud’s Data and AI professional services across North America. Partner with account executives to identify opportunities, understand customer requirements, architect AWS-based data and AI solutions, deliver demonstrations and proof-of-concepts, and develop technical proposals. Build relationships with technical and executive stakeholders, support complex million-dollar-plus deals, drive expansion opportunities, and collaborate with delivery, legal, procurement, and sales teams to achieve targets.

Design, develop, and maintain AWS- and Databricks-based data platforms and ETL/ELT pipelines using Python, PySpark, Snowflake, and Delta Lake. Manage Medallion architecture, cloud infrastructure, data warehouses, performance tuning, data modeling, governance, metadata, permissions, lineage, and automated data quality testing. Collaborate with analysts, product managers, and data scientists while troubleshooting distributed systems and database bottlenecks.

Designs, develops, deploys, and optimizes large-scale data pipelines across cloud and on-premise environments. Ingests high-volume data, analyzes datasets with Data Scientists, and builds data solutions for data warehouse and AI projects. The role requires Python, Spark, dbt, pipeline orchestration, container platforms, advanced data formats, AWS data services, and BI tool integration, along with strong analytical, collaborative, and independent working skills.

Design and build cloud-native data platforms and lakehouse architectures on AWS. Develop and maintain ETL/ELT and streaming pipelines, support RAG/vector search and ML integrations, ensure data modeling, governance, observability, troubleshooting, and migration from legacy ETL to cloud warehouses while engaging with customers and stakeholders.

Lead end-to-end delivery of Salesforce CRM programs: plan roadmaps, manage budgets, mitigate risks, coordinate cross-functional teams, drive change management and ensure solution quality and adoption.

Design, develop, and scale backend platforms and AI-driven workflows. Build resilient distributed systems and microservices, integrate LLMs (AWS Bedrock), implement event-driven architectures and APIs, optimize databases, caching, and search, contribute to cloud infrastructure, CI/CD, observability, and provide technical leadership and mentoring.

Design, build, and automate complex AWS cloud environments using IaC and container platforms. Implement CI/CD, monitoring, and centralized logging; troubleshoot issues; collaborate with customers to deliver tailored, scalable solutions.

Lead discovery, design, configuration, and implementation of Salesforce solutions across Sales, Service, and Experience Clouds. Translate business requirements into scalable designs, produce SDD/FRD, apply architecture and integration best practices, mentor junior consultants, drive Agile project delivery, and perform QA and ongoing support.

Lead Salesforce implementation projects: gather and prioritize business requirements, design solutions, configure Salesforce per SOW, create and manage project documentation, set deadlines, coordinate with clients and success teams, and support launch and adoption.

Design and optimize Salesforce solutions for managed-services clients: lead discovery, architect platform implementations, remediate technical debt, improve performance and scalability, mentor teams, resolve escalations, and implement AI-driven features (Einstein/Agentforce).

Design and deliver Salesforce technical solutions and integrations. Lead technical workshops, create solution designs, specifications and user stories, implement Apex, Visualforce and LWC, integrate Salesforce with external systems via SOAP/REST and ETL tools, mentor teams, and ensure on-time, on-budget delivery and customer success.

Consult with clients to design, configure, and implement Salesforce solutions. Lead requirements workshops, translate needs into user stories, manage integrations, migrations, UAT, training, and oversee development teams. Emphasizes governance, technical debt management, deployment tooling, and ensuring ongoing client success.

Design and present tailored Salesforce solutions in pre-sales: lead discovery, architect multi-cloud (Data Cloud, MuleSoft, CPQ, AI) roadmaps, build PoCs/demos, create SOWs/proposals, and hand off to delivery while representing AllCloud at events.

Serve as a strategic HR Business Partner for German/DACH teams: advise managers on organization, culture, employee relations, change management, career development, compensation benchmarking, and compliance with DACH labor law while collaborating with global HR, finance, legal, and IT.

Lead end-to-end delivery of large-scale AWS and Generative AI programs, manage budgets and resources, author BRDs, oversee integrations and security/compliance, manage stakeholders, mitigate risks, and track KPIs across enterprise and defense environments.

Design, build, and operate ML and data infrastructure on AWS. Migrate and optimize databases and ETL pipelines, build data lakes, process structured and unstructured data, develop and deploy ML and deep learning models (Jupyter), leverage AWS AI/ML services, and support customer-facing data solutions and migrations.

Design, develop, and deploy Python-based backend services on AWS; build event-driven, high-availability systems using Step Functions, Lambda, API Gateway, DynamoDB/Aurora/Postgres, S3; integrate LLMs via AWS Bedrock and RAG frameworks; use Docker/Kubernetes/EKS and Kafka; implement observability with CloudWatch/X-Ray; collaborate with cross-functional teams to deliver AI-powered production systems.