Looking for a job

Saritha Reddy Kamasani

@sarithareddykamasani

Data Engineer at Abbott building Spark pipelines and streaming analytics that cut data latency.

At Abbott, I designed and optimized Spark and PySpark jobs in Databricks, improving data processing efficiency by 30%. I also developed dimensional data models and implemented data quality and lineage frameworks across production pipelines.

I’ve built and maintained data pipelines at GoldmanSachs, IBM, and General Electric, using Airflow to orchestrate workflows and Kafka or Kinesis for streaming. At IBM, I managed more than 50 Airflow DAGs supporting data ingestion and transformation.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Abbott

Jul 2025 - Present (1 year 2 months)

Designed and optimized over 15 Spark and PySpark jobs within Databricks, improving data processing efficiency by 30%. Developed dimensional data models and implemented data quality frameworks across production pipelines.

Spark PySpark Databricks SQL Airflow Kafka Spark Structured Streaming Monte Carlo Data Quality Dimensional Data Models Medallion Architecture

Data Engineer

Goldman Sachs

Jan 2024 - Jun 2025 (1 year 5 months)

Developed over 30 Spark and Scala data transformation jobs optimized with SQL, improving processing efficiency by 25%. Maintained production data pipelines and Airflow orchestration workflows.

Spark Scala SQL Airflow Kinesis Spark Structured Streaming Data Quality Dimensional Data Models

Data Engineer

IBM

Dec 2020 - Jul 2022 (1 year 7 months)

Built over 15 SparkSQL jobs using Python and SQL to process and cleanse large datasets. Engineered production data pipelines on cloud data lakes using Databricks, ingesting over 5 TB monthly.

SparkSQL Python SQL Databricks Airflow Medallion Architecture Data Modeling

Bigdata Developer

General Electric

Jun 2018 - May 2020 (1 year 11 months)

Developed over 20 PySpark data transformation jobs using Python and Spark, improving processing efficiency by 30%. Designed scalable SQL schema and dimensional data models for Databricks and Azure Data Lake.