Data Modeling:

• Design, Develop and maintain conceptual, logical, and physical data models for the data blueprints.
• Implement and enforce data modeling standards to ensure consistency and accuracy across the data stores and platforms.
• Design Master & Reference Data Management strategies.
• Implement Metadata Management and end-to-end Data Lineage.
• Act as Solution Architecture Review & Design Authority.
• Design Cloud & Hybrid Data Architecture and migration strategies.

• Design and implement robust, scalable, and secure data platform solutions using Snowflake, SQL Server, and cloud-native services.
• Collaborate with stakeholders to translate business and analytics needs into efficient data platform architecture.
• Define and implement data storage, access, and lifecycle management strategies across structured and semi-structured data.

• Review the existing enterprise data ecosystem to identify architectural gaps, technical debt, scalability limitations, and opportunities for modernization and continuous improvement.
• Architect end-to-end enterprise solutions by collaborating with business and technical stakeholders to translate business requirements into solution blueprints, feasibility assessments, High-Level Designs (HLDs), and Low-Level Designs (LLDs).
• Develop and maintain enterprise architecture artifacts using industry-standard tools such as Bizzdesign, ArchiMate, erwin Data Modeler/Evolve, and other Enterprise Architecture modeling platforms.
• Lead architecture governance by conducting solution, implementation, and deployment reviews to ensure alignment with enterprise architecture standards, scalability, security, reliability, and operational excellence.
• Serve as the subject matter expert for Microsoft SSIS, designing and developing enterprise-grade ETL solutions utilizing metadata-driven frameworks, CDC/SCD implementations, complex transformations, Script Components, parallel processing, enterprise logging, deployment automation, and performance optimization.
• Define enterprise performance standards, Service Level Objectives (SLOs), and Key Performance Indicators (KPIs) for data platforms, ETL pipelines, databases, APIs, and integration services.
• Lead performance engineering and capacity planning initiatives by establishing performance baselines, conducting workload analysis, identifying bottlenecks, and implementing optimization strategies across ETL pipelines, SQL workloads, cloud services, APIs, and databases.
• Optimize platform performance through query tuning, indexing, partitioning, caching, resource governance, and pipeline optimization while ensuring scalability, reliability, cost efficiency, and optimal resource utilization.
• Establish performance testing methodologies and continuous monitoring frameworks to proactively assess platform health, predict capacity requirements, and ensure sustained operational excellence.
• Collaborate with engineering, infrastructure, and DevOps teams to continuously enhance platform performance, deployment strategies, and solution reliability while balancing business requirements, scalability, and operational costs.

• Design, implement, and govern High Availability (HA) and Disaster Recovery (DR) architectures to ensure business continuity and platform resilience.
• Define enterprise standards for database replication, clustering, failover mechanisms, backup, and recovery strategies across cloud and on-premises environments.
• Evaluate and optimize SQL Server Always On Availability Groups, replication technologies, and cloud-native HA solutions to meet Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO).
• Establish resilience testing frameworks by conducting periodic failover, disaster recovery, and business continuity exercises to validate platform readiness.
• Collaborate with infrastructure and operations teams to continuously improve system availability, reliability, and fault tolerance.

• Implement role-based access control, encryption, and data masking in compliance with data governance standards.
• Partner with security teams to ensure the platform aligns with enterprise compliance and audit requirements.
• Define and enforce Data Governance aligned with DAMA-DMBOK, NDMO, SDAIA and PDPL.

• Set up and maintain real-time monitoring using tools such as Prometheus, Grafana, and native cloud monitoring tools.
• Provide transparency into data jobs, pipeline statuses, and platform health.

• Partner with data scientists, analysts, and business stakeholders to support self-service analytics and data product development.
• Document architecture, data flow, and technical decisions using industry best practices.

• Continuously monitor and evaluate emerging technologies, trends, and best practices within the data and cloud ecosystem.
• Evaluate new tools and recommend integration to enhance platform scalability, performance, and cost-effectiveness.

Qualifications:

• Bachelor’s or Master’s in Computer Science, Information Systems, or a related field.
• 10+ years of experience in data engineering, data platform, or analytics engineering roles.
• Proficient in SQL Server, SSIS, Oracle SSIS, IBM AppConnect APIs, IBM MQ and Elastic Search.
• Strong SQL, Python, and shell scripting capabilities.
• Experience building data transformation pipelines using modern technologies and platforms.
• Familiar with data governance frameworks (e.g., DAMA-DMBOK), security, and data privacy regulations.
• Knowledge of CI/CD practices and Git-based version control.
• Excellent communication and collaboration skills.
• Comfortable leading initiatives and mentoring colleagues and team members.