Candidates with 10 to 12+ years of experience with Azure SQL, Responsible for Architecting, designing, hands on Database management.
Solution Design & Architecture
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Design scalable Azure SQL architectures
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Recommend optimal architecture based on:
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Application Workload
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Concurrency
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Data volume
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Hands-On experience in Azure Database Migration Service (DMS) for large databases
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Ability to manage large volumes of Data
Data Model & Schema Design
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Redesign schema for:
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Apply:
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Normalization vs denormalization trade-offs
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Partitioning strategies
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Data archiving strategies
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Define:
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Data lifecycle management
Workload & Capacity Planning
o High concurrency systems
o Burst workloads
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
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)
o Efficient data movement
Mandatory SQL Server / Azure SQL DBA Expertise
Candidate must have strong hands-on experience in:
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Microsoft SQL Server and Azure SQL Database administration
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Database sizing, file growth, storage management, tempdb, transaction log management
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Backup and restore strategies: full, differential, transaction log, copy-only, point-in-time recovery
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Recovery models: simple, full, bulk-logged
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High availability and disaster recovery concepts
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Database maintenance: index rebuild/reorganize, statistics update, integrity checks
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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:
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Analyze actual and estimated execution plans
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Identify table scans, index scans, key lookups, missing indexes, implicit conversions, parameter sniffing, bad joins, spills, incorrect cardinality estimates, and outdated statistics
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Tune stored procedures, views, joins, aggregations, and ETL queries
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Diagnose blocking, deadlocks, wait types, CPU pressure, memory pressure, and I/O bottlenecks
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Use Query Store, DMVs, Extended Events, SQL Profiler where applicable, Azure Query Performance Insight, and Azure Monitor
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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:
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Designing robust ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, SSIS, or equivalent tools
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Handling schema evolution when source systems add, remove, or change columns
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Designing schema enforcement and schema drift handling strategies
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Implementing staging, landing, quarantine/error tables, reject records, audit tables, and reconciliation checks
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Designing retry, restartability, idempotency, and failure recovery mechanisms
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Handling bad source records without failing the complete load where business rules allow
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Implementing data validation, data profiling, duplicate handling, null checks, referential integrity checks, and data quality rules
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Working with multiple source systems and designing scalable ingestion frameworks
Data Warehouse & Dimensional Modeling
Candidate must be able to:
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Explain and design OLTP vs OLAP architecture
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Design star schema and snowflake schema models
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Define fact tables, dimension tables, measures, surrogate keys, slowly changing dimensions, and conformed dimensions
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Define grain of fact tables and explain how grain impacts measures, aggregation, and reporting accuracy
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Design data marts and reporting layers for Power BI or similar BI tools
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Separate operational workloads from analytical workloads using appropriate architecture patterns
Database Technology Selection
Candidate should be able to:
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Compare SQL and NoSQL databases
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Recommend appropriate database technology based on data model, consistency, scalability, transaction requirements, query patterns, and reporting needs
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Understand use cases for relational databases, document databases, key-value stores, columnar stores, and analytical stores
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Explain trade-offs between ACID consistency, schema flexibility, performance, and scalability
DevOps & Automation
- CI/CD pipelines for DB deployments
- Perform database provisioning, configuration, and upgrades
Monitoring & Troubleshooting
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Using tools like
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Azure Monitor
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Log Analytics
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Query Performance Insight
Troubleshoot database issues-
Connectivity
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Performance bottlenecks
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Failures
Mandatory Sub Skill: Power BI or any other reporting tool
Qualifications
B.E, B.Tec
