Senior Data Engineering Architect

About

I’ve always been drawn to systems that are messy, fragile, or misunderstood. The more complex the environment, the more interesting the challenge. Over time, that curiosity turned into a career focused on transforming scattered data, legacy code, and manual processes into clean, automated, intelligent platforms.

My engineering interests extend beyond data pipelines. I enjoy building modern apps, microservices, and lightweight automation tools that solve real problems for teams. Whether it’s a service that monitors pipelines, an API that compares databases, or an internal app that converts legacy code into modern engines, I like creating software that feels purposeful and practical.

One of my most impactful contributions has been designing an automated framework that converts legacy SAS scripts into PySpark pipelines. It eliminates manual rewrites, reduces migration time, and gives engineers a predictable path from old systems to modern cloud-native architectures.

Today, my work lives at the intersection of cloud engineering, AI-driven automation, microservice design, and modern analytics. I’m especially focused on agentic AI — building systems that can watch, reason, and assist engineers and business users instead of just waiting to be queried.

Career Journey

My journey has been shaped by a desire to build things that make life easier for engineers, analysts, and business teams. I’ve moved from BI and reporting into deep data engineering, architecture, AI automation, and microservice development. Along the way, I’ve built tools that compare entire databases, frameworks that translate old code into modern engines, and apps that bring intelligence directly into analytics workflows.

Experian Services — Cloud Engineer & Solution Architect

In my current contract engagement with Experian, I serve as a Cloud Engineer and Solution Architect, focusing on enterprise data modernization and cloud transformation initiatives. I design and implement scalable solutions across AWS,Snowflake, PySpark, and modern data platforms, transforming complex legacy workloads into automated cloud-based architectures. I designed a reusable framework for converting SAS workloads to PySpark and modernizing DataStage pipelines using AWS Glue and Snowflake. I also develop automation services for SQL validation, metadata extraction, technical documentation, and database comparison. One of the key solutions automates the analysis of schema differences, lineage gaps, and data quality issues between legacy platforms and Snowflake, transforming weeks of manual analysis into a streamlined, guided workflow.

I also built microservices that automated SQL validation, metadata extraction, and documentation. One of the most impactful was a database comparison service that analyzed schema differences, lineage gaps, and data quality issues between legacy systems and Snowflake. It turned weeks of manual analysis into a guided, automated workflow.

Germania Insurance — BI Lead / Data Engineer

At Germania, I focused on making analytics feel less like reporting and more like insight. I integrated AI into dashboards to generate narrative commentary, flag anomalies, and surface risks. I also built internal apps and microservices that supported ingestion, quality checks, and automated KPI monitoring.

Earlier Roles

My earlier work across ThermoFisher, GM, Deloitte, and Accenture gave me the architectural discipline I still rely on: dimensional modeling, warehouse design, BI modernization, and large-scale system migrations. Those environments taught me how to build systems that don’t just work once, but keep working as the business grows and changes.

Featured Engineering Work

Selected examples of enterprise modernization, data engineering automation, cloud architecture, and AI-assisted engineering.

Engineering Philosophy

I believe great data systems are quiet. They don’t draw attention to themselves. They run predictably, scale gracefully, and make life easier for the people who depend on them. My job is to build those systems — the ones that disappear into the background because they simply work.

I also believe AI belongs inside engineering, not outside it. Not as a replacement for human judgment, but as a tool that amplifies it. AI can validate pipelines, generate documentation, detect anomalies, and accelerate development — but only when guided by engineers who understand the craft.

At the end of the day, I’m not trying to build the flashiest systems. I’m trying to build the ones that last.

Architecture & Engineering Expertise

AI Development & Automation

• AI-assisted SQL optimization, validation, and documentation
• Agentic AI prototypes for pipeline monitoring and remediation
• Automated metadata extraction and lineage mapping
• AI-powered dashboard commentary and anomaly detection
• Database comparison engines for migration and modernization
• Frameworks to convert SAS scripts into PySpark pipelines

App & Microservice Development

• Building internal apps for analytics, automation, and engineering workflows
• Designing microservices for metadata intelligence, SQL validation, and ingestion
• Developing lightweight APIs for database comparison and schema analysis
• Creating automation tools that support migration, quality checks, and monitoring

Cloud & Data Engineering

• Snowflake ingestion frameworks (Snowpipe, Streams, Tasks)
• AWS Glue, Lambda, Step Functions, Athena
• PostgreSQL, PL/pgSQL, T-SQL, SQL MI
• Python, PySpark, dbt

Enterprise Analytics

• Power BI, Tableau, Cognos
• AI-integrated dashboards for business insights
• Semantic layer design and KPI modeling

Research & Thought Leadership

I’m currently writing research papers on Modern Agentic AI and how autonomous systems can reshape industries like insurance, fintech, manufacturing, and enterprise operations. My work explores how agents can plan, act, evaluate, and collaborate with human engineers.

Research Focus

• Agentic AI for pipeline monitoring and automated remediation
• AI-driven metadata intelligence for large-scale migrations
• Autonomous SQL tuning and query optimization
• AI-powered business commentary for dashboards
• Cross-industry applications of agentic systems

My goal is to build frameworks where AI becomes a reliable engineering partner — not a novelty, but a practical tool that improves quality, speed, and clarity across enterprise data systems.

Contact

Let’s connect professionally.

• Email: satya.nagisetti@outlook.com
• LinkedIn: LinkedIn Profile
• GitHub: GitHub
• Location: Austin, Texas