PAVANSAI ACHIMSETTY
@pavansaiachimsetty
Data engineer building scalable cloud-native platforms for analytics and AI.
What I'm looking for
I am a data engineer with 5+ years designing and operating cloud-native data platforms across financial and healthcare domains, focused on reliable, secure, and compliant data systems that enable analytics and ML/GenAI use cases.
I have built end-to-end ETL/ELT and near-real-time ingestion pipelines using AWS (Glue, S3, Kinesis, Redshift, Bedrock) and Azure (Data Factory, Synapse, Databricks), improved query and ETL performance, and implemented production controls for data quality, observability, and cost optimization.
I partner closely with data science, product, and business teams to translate requirements into analytics-ready models and feature pipelines, delivering measurable outcomes such as reduced latency, cost savings, and accelerated data-driven decision-making.
Experience
Work history, roles, and key accomplishments
Data Engineer
Fidelity
Jan 2024 - Present (2 years 1 month)
Architected and operated AWS-native ingestion and ETL/ELT pipelines supporting fraud analytics and ML workloads, achieving 99.9% data reliability and accelerating data-driven decision-making by 25%; delivered analytics-ready models that produced $3K weekly cost savings.
Data Engineer
Philips Healthcare
Mar 2019 - Jul 2022 (3 years 4 months)
Built Azure-native analytics platforms and scalable batch pipelines using ADF, Databricks, and Synapse, reducing ETL execution time by 30% and cloud spend by 20–25% while improving data quality incident rates by 30–35%.
Education
Degrees, certifications, and relevant coursework
University of the Cumberlands
Master of Science, Information Technology
Master of Science in Information Technology completed in December 2024, focusing on advanced topics in information systems and data engineering.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
Skills
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