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 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

Database Technology Selection

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

DevOps & Automation

·       CI/CD pipelines for DB deployments

·       Perform database provisioning, configuration, and upgrades

 

 Monitoring & Troubleshooting

• • • 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

Qualifications

B.E, B.Tec

Additional Information