Data Solution Architect

Job Description Job Description: Data Solution Architect (with Proposal & Pre-Sales Responsibilities) Overview The Data Solution Architect is responsible for designing scalable and secure data solutions while also supporting pre-sales activities, proposal development, and client engagement. Key Responsibilities - Solution Design & Architecture - Design end-to-end data architectures including data lakes, data warehouses, lakehouses, and real-time pipelines. - Develop reusable architectural patterns, reference models, and solution frameworks. - Evaluate emerging technologies and recommend optimal components for each solution. - Proposal Development & Pre-Sales Support - Collaborate with sales teams to understand client needs and craft compelling technical proposals. - Develop solution blueprints, technical write-ups, effort estimations, and pricing inputs. - Participating in RFP/RFI responses, ensuring architecture, scope, and approach are clearly articulated. - Deliver technical presentations, demos, and workshops to clients. - Translate business problems into clear, value-driven technical solutions. - Client Engagement - Work closely with clients to understand data challenges, requirements, and long-term goals. - Serve as the technical point of contact during discovery sessions and pre-sales discussions. - Build trust with executive stakeholders by articulating the value and feasibility of proposed data solutions. - Data Platform Development - Guide the implementation of modern cloud-based data platforms (AWS, Azure, or GCP). - Oversee engineering teams in building robust ETL/ELT pipelines, data models, and integration layers. - Ensure solutions adhere to performance, quality, and reliability standards. - Governance, Security & Compliance - Define data governance strategies, security frameworks, and compliance controls. - Ensure architectural adherence to standards such as GDPR, HIPAA, or industry-specific regulations. - Establish best practices for data lifecycle management, access policies, and metadata management. - DevOps & DataOps Engineering - Design CI/CD pipelines for data pipelines, models, and platform components (e.g., Azure DevOps, GitHub Actions, Jenkins). - Define standard automated deployment processes for ETL/ELT workflows, streaming applications, and analytical models. - Define monitoring, logging, and alerting data workloads using tools such as Prometheus, Grafana, CloudWatch, or Azure Monitor. - Ensure platform reliability, scalability, and observability through automation and performance tuning. - Performance Optimization & Innovation - Review architecture performance and guide optimization for scalability and cost-efficiency. - Lead proof-of-concepts (POCs) to validate new technologies or solution approaches. - Continuously evaluate and integrate modern data engineering and AI/ML trends. Qualifications Education & Experience - Bachelor's or master's degree in computer science, Information Systems, Data Engineering, or related field. - 6+ years of experience in data engineering, analytics platform design, or solution architecture. - Prior experience supporting pre-sales, proposal creation, or client solutions is highly desirable. Technical Skills - Strong hands-on expertise with cloud data platforms (Snowflake, Databricks, BigQuery, Redshift, Synapse). - Deep understanding of data modeling, ETL/ELT frameworks, and distributed data processing (Spark, Kafka). - Proficiency in SQL and Python; experience with CI/CD, IaC (Terraform), and containerization (Docker/Kubernetes). - Knowledge of BI tools (Power BI, Tableau, Looker) and ML ecosystem is a plus. Soft Skills - Excellent written communication for producing technical proposals and solution documents. - Strong presentation and pre-sales communication skills with the ability to engage senior stakeholders. - Ability to work cross-functionally with sales, engineering, and business teams. - Analytical mindset with strong problem-solving skills.