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ammarkhan935

Ammar

@ammarkhan935

Efficient Databases Smarter Data Powered by AI

Canada
English
About me
Hi, I'm Ammar, a Database Administrator and Data Specialist helping businesses build reliable, scalable data systems. I offer solutions across four areas: Database Administration (tuning, security, and maintenance across relational platforms); Data Analytics & Dashboards (turning raw data into clear insights); Data Engineering & ETL Pipelines (automated, scalable data workflows); and AI-Powered Data Solutions (applying AI to add intelligence to your data). Whether it's a database, dashboard, or pipeline, I deliver clean, reliable solutions tailored to your needs. Message me to get started.... Read more

Skills

a
ammarkhan935
Ammar
Offline • 
Average response time: 1 hour

See my services

Database Administration (DBA)
I will be your database developer dba
Data Analytics Consultation
I will act as your data analyst

Work experience

Consultant Database Engineer

Longview Marketing • Full-time

Apr 2026 - Present2 mos

Managed and maintained SQL Server database environments, ensuring high availability, performance, security, and reliability. Performed database monitoring, backup and recovery, performance tuning, troubleshooting, and patch management. Supported production and non-production environments, collaborated with cross-functional teams to resolve database issues, and implemented best practices for database administration, maintenance, and optimization. Assisted with automation, documentation, and operational support to improve efficiency and system stability.

SYSTEMIQ

Consultant DBA & BI Developer

SYSTEMIQ • Full-time

Nov 2021 - Apr 20264 yrs 5 mos

Managed and authored clean, efficient T-SQL and PL/SQL queries to provide backend database support to development teams and facilitate seamless application performance. This included delivering comprehensive database support for high-profile projects such as Etisalat UAE, RTA Abu Dhabi, Zong, and Telenor. Core responsibilities involved developing and optimizing complex stored procedures, views, triggers, and other database objects to meet project requirements while driving system performance. To ensure data integrity and reliability, duties extended to conducting performance monitoring, security audits, troubleshooting, implementing robust backup and recovery solutions, and maintaining detailed database change logs to systematically track development updates. Additionally, key functions focused on data analytics by designing dynamic Power BI dashboards and reports—particularly for the HEC project—utilizing SQL queries to extract data from live sources. This involved defining robust data models by establishing relationships between facts and dimensions, processing large datasets from multiple sources using Power Query in Power BI and Excel for efficient data transformations, and integrating reports with live sources to enable automatic, scheduled refreshes for up-to-date stakeholder reporting.

Starz

Data & BI Engineer

Starz • Full-time

Oct 2016 - Sep 20214 yrs 11 mos

Managed and engineered end-to-end data pipelines and business intelligence solutions for international clients using Azure, SQL, DAX, Python, and Power BI. Core responsibilities involved designing, building, and managing scalable ETL/ELT pipelines in Azure Data Factory (ADF) to orchestrate bulk data movement into cloud data layers. To support schema evolution and seamless development integration, duties included modernizing data structures by redesigning ERDs, authoring DDL/DML queries, and utilizing Python scripts to dynamically generate schemas across multiple databases. Additionally, advanced workflows were introduced by integrating AI models into ingestion processes to enhance automation and deploying Power Apps for process orchestration and data capture. On the BI and analytics front, work focused heavily on turning raw pipeline data into actionable business insights by designing and developing interactive Power BI dashboards, reports, and comprehensive KPI frameworks. This involved writing complex, optimized queries and backend logic to support automated reporting layers, utilizing advanced DAX and Excel for deep-dive metrics, and defining structured data models that enabled executive decision-making. By bridging the gap between data engineering and business intelligence, these efforts successfully automated data workflows from ingestion to final visualization, ensuring high performance, efficient transformations, and real-time reporting availability for stakeholders.