Presales Specialist

**Job Title: Presales - Cloud & Data Architect**

**Location: Remote**

Our Client is at the forefront of providing innovative data analytics solutions that empower businesses to harness the power of their data. We are looking for a dynamic Cloud Data Architect to join our team in a presales role, focusing on solution-based selling with a consultative mindset. This position is an excellent opportunity to demonstrate thought leadership, leverage deep data expertise, and influence the future of our capabilities and offerings in Cloud, Advanced Analytics, and AI technologies.

As a Cloud Data Architect in a presales role, you will be pivotal in engaging with potential clients, understanding their needs, and designing solutions that showcase the value of our advanced analytics and AI capabilities. Working closely with our business partners, you will advocate for the use of modern data technologies to solve business problems, deliver cutting-edge solutions, and build strong relationships with our clients and member-partners.

**Key Responsibilities**:
**1. Cloud Platform Proficiency**
- Demonstrate in-depth knowledge of at least two major cloud platforms (Azure, AWS, Google Cloud), including their storage, databases, analytics, machine learning, and networking services.
- Maintain relevant certifications such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect, and Google Professional Cloud Architect.

**2. Data Architecture and Modeling**
- Design and implement cloud-based data warehousing solutions (e.g., Amazon Redshift, Azure Synapse Analytics, Google BigQuery).
- Develop and manage data lakes using services like AWS Lake Formation, Azure Data Lake Storage, and Google Cloud Storage.
- Create logical and physical data models to support complex analytics requirements.

**3. ETL/ELT Processes**
- Utilize data integration tools (AWS Glue, Azure Data Factory, Google Cloud Dataflow) to design and implement efficient ETL/ELT processes.
- Build scalable data pipelines for extracting, transforming, and loading data from diverse sources.

**4. Big Data and Analytics**
- Leverage big data technologies (Hadoop, Spark, Kafka) and their cloud-managed services (AWS EMR, Azure HDInsight, Google Dataproc).
- Implement real-time data processing and analytics using AWS Kinesis, Azure Stream Analytics, and Google Cloud Pub/Sub.

**5. Security and Compliance**
- Ensure data security at rest and in transit through encryption, IAM, and data masking.
- Implement solutions adhering to compliance standards like GDPR, HIPAA, and SOC 2.

**6. Machine Learning and AI**
- Integrate machine learning services (AWS SageMaker, Azure Machine Learning, Google AI Platform) into data analytics workflows.
- Design data pipelines that incorporate machine learning models.

**7. Performance Optimization**
- Optimize the cost and performance of cloud services by selecting appropriate services and resource sizing.
- Perform performance tuning of databases, queries, and data workflows to meet SLAs.

**8. DevOps and Automation**
- Implement Infrastructure as Code using AWS CloudFormation, Azure Resource Manager, and Google Deployment Manager.

**9. Collaboration and Communication**
- Engage with stakeholders to gather requirements and translate them into technical solutions.
- Lead and mentor a team of data engineers, analysts, and developers.

**10. Industry Knowledge**

**Future Duties and Responsibilities**:

- **Data Engineering**: Build, own, and run data engineering pipelines, workflows, and standardized practices for delivering new products and capabilities using Data and Cloud technologies, including data transformation, processing, and analysis.
- **Solution Architecture**: Participate in all aspects of data solution architecture and design using cloud technologies (GCP, AWS, Azure).
- **Architectural Strategies**: Develop strategies for data modeling, design, and implementation to solve business problems as outlined in client requests and bid proposals (RFP, RFS, RFI).
- **Client Presentations**: Develop presentations to support conversations and deep dives in Advanced Analytics, Data Warehousing, Big Data, and AI.
- **Pre-Sales Strategies**: Develop and execute strategies to identify new business opportunities, engage with potential clients, and position the analytics and AI practice as a trusted advisor.
- **Technology Research**: Research new technologies, frameworks, and thought leadership within the industry to incorporate into company data architectures and go-to-market material.
- **Big Data Integration**: Ensure Big Data practices integrate into overall data architectures and data management principles (e.g., data governance, data security, metadata, data quality).
- **Discovery Workshops**: Facilitate workshops, articulate value propositions, build solution architecture with roadmaps, and communicate business value to clients.
- **Best Practices**: Establish best practices and consistency across variou