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.

• Design and implement enterprise-grade data pipelines utilizing Microsoft SSIS and proprietary integration platforms, ensuring scalability, reliability, maintainability, and operational excellence.
• Orchestrate and monitor data workflows, ensuring data reliability and availability.
• Implement fault-tolerant, modular pipelines with robust logging and alerting mechanisms.
• Tune performance of data workflows, SQL queries, and cloud services.
• Leverage indexing, partitioning, caching, and pipeline optimizations to ensure platform efficiency.
• Define enterprise performance benchmarks, service level objectives (SLOs), and key performance indicators (KPIs) for data platforms, databases, and integration services.
• Lead performance assessments and capacity planning initiatives to ensure scalability, reliability, and optimal resource utilization.
• Establish performance testing methodologies for ETL pipelines, APIs, databases, and analytical workloads using industry best practices.
• Analyze workload patterns and implement optimization strategies including indexing, partitioning, query tuning, caching, and resource governance.
• Develop performance baselines and proactively identify bottlenecks through continuous monitoring, trend analysis, and predictive capacity planning.
• Collaborate with engineering teams to optimize platform performance while balancing scalability, operational costs, and business requirements.

• 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.
• 7+ 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.