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
- 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
- Redesign schema for: Performance, Scalability
- Apply: Normalization vs denormalization trade-offs, Partitioning strategies, Data archiving strategies
- Define: Data lifecycle management
- Design solutions for: High concurrency systems, Burst workloads
- Recommend: vCore vs DTU models, Serverless vs provisioned, Elastic pool strategies
- Analyze current spend and design: Right-sized compute tiers, Auto-scaling strategies
- Recommend: Serverless for intermittent workloads, Optimize Infrastructure resources for Elastic pools based on Data I/O
- Design integrations with: Azure Data Factory, Synapse Analytics, Event-driven pipelines (Event Hub, Service Bus)
- Ensure: Efficient data movement, Minimal latency
Requirements
- 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
- 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
- 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
- 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 reporting accuracy
- Design data marts and reporting layers for Power BI or similar BI tools
- Separate operational workloads from analytical workloads using appropriate architecture patterns
- Candidate should be able to: Compare SQL and NoSQL databases
- Recommend appropriate database technology based on data model, consistency, scalability, transaction requirements, query patterns, and reporting needs
- Understand use cases for relational databases, document databases, key-value stores, columnar stores, and analytical stores
- Explain trade-offs between ACID consistency, schema flexibility, performance, and scalability
- CI/CD pipelines for DB deployments
- Perform database provisioning, configuration, and upgrades
- Using tools like: Azure Monitor, Log Analytics, Query Performance Insight
- Troubleshoot database issues: Connectivity, Performance bottlenecks, Failures
- Mandatory Sub Skill: Power BI or any other reporting tool
