Senior Data Architect

Job Description Roles and Responsibilities. • Proficiency in data modeling and design, including SQL development and database administration • Develop and document business process models to illustrate current and future states • Analyze data to identify trends, patterns, and insights that inform business decisions • Propose and design technical and process solutions that meet business needs and objectives • Deliver insights on potential areas of growth, optimization, and improvements • Problem-Solving. The ability to identify problems, analyze potential solutions, and implement the most effective solution is critical in data analysis projects • Support business intelligence strategies with quantitative analysis • Ability to implement common data management and reporting technologies, as well as the basics of columnar and NoSQL databases, data visualization, unstructured data, and predictive analytics • The ability to create interactive dashboards and detailed reports presenting data insights in a clear and accessible manner to stakeholders • Strong communication skills (oral and written) Qualifications Candidates with 10 to 12+ years of experience with Azure SQL, Responsible for Architecting, designing, hands on Database management. Solution Design & Architecture Design scalable Azure SQL architectures Recommend optimal architecture based on: Application Workload Concurrency Data volume Hands-On experience in Azure Database Migration Service (DMS) for large databases Ability to manage large volumes of Data Data Model & Schema Design Redesign schema for: Performance Scalability Apply: Normalization vs denormalization trade-offs Partitioning strategies Data archiving strategies Define: Data lifecycle management Workload & Capacity Planning · Design solutions for: o High concurrency systems o Burst workloads · Recommend: o vCore vs DTU models o Serverless vs provisioned o Elastic pool strategies Cost Optimization Architecture · Analyze current spend and design: o Right-sized compute tiers o Auto-scaling strategies · Recommend: o Serverless for intermittent workloads o Optimize Infrastructure resources for Elastic pools based on Data I/O Data Integration & Ecosystem Design · Design integrations with: o Azure Data Factory o Synapse Analytics o Event-driven pipelines (Event Hub, Service Bus) · Ensure: o Efficient data movement Minimal latency Mandatory SQL Server / Azure SQL DBA Expertise Candidate must have strong hands-on experience in: Microsoft SQL Server and Azure SQL Database administration Database sizing, file growth, storage management, tempdb, transaction log management Backup and restore strategies: full, differential, transaction log, copy-only, point-in-time recovery Recovery models: simple, full, bulk-logged High availability and disaster recovery concepts Database maintenance: index rebuild/reorganize, statistics update, integrity checks Handling production incidents related to space, logs, blocking, deadlocks, slow queries, failed jobs, and ETL failures Query Performance Tuning & Execution Plan Analysis Candidate must be able to: Analyze actual and estimated execution plans Identify table scans, index scans, key lookups, missing indexes, implicit conversions, parameter sniffing, bad joins, spills, incorrect cardinality estimates, and outdated statistics Tune stored procedures, views, joins, aggregations, and ETL queries Diagnose blocking, deadlocks, wait types, CPU pressure, memory pressure, and I/O bottlenecks Use Query Store, DMVs, Extended Events, SQL Profiler where applicable, Azure Query Performance Insight, and Azure Monitor Explain cases where query tuning does not improve performance and identify non-query bottlenecks such as storage, network, concurrency, locking, application design, or resource tier limitations Data Integration, ETL Architecture & Data Quality Candidate should have hands-on experience in: Designing robust ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, SSIS, or equivalent tools Handling schema evolution when source systems add, remove, or change columns Designing schema enforcement and schema drift handling strategies Implementing staging, landing, quarantine/error tables, reject records, audit tables, and reconciliation checks Designing retry, restartability, idempotency, and failure recovery mechanisms Handling bad source records without failing the complete load where business rules allow Implementing data validation, data profiling, duplicate handling, null checks, referential integrity checks, and data quality rules Working with multiple source systems and designing scalable ingestion frameworks Data Warehouse & Dimensional Modeling Candidate must be able to: Explain and design OLTP vs OLAP architecture Design star schema and snowflake schema models Define fact tables, dimension tables, measures, surrogate keys, slowly changing dimensions, and conformed dimensions Define grain of fact tables and explain how grain impacts measures, aggregation, and re