Senior Data Architect

Job description

Endava 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) 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