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I am Deepak Rambarki, a Data Engineer with 5+ years of experience building cloud-native data platforms and real-time pipelines. I specialize in distributed systems, including Spark, Kafka, and Snowflake, and partner with finance and risk teams to deliver scalable, reliable data solutions that support regulatory reporting and business insights. I’m passionate about data quality, observability, and DataOps. I enjoy mentoring junior engineers and collaborating with cross-functional teams to translate complex requirements into robust architectures that improve time-to-insight and operational excellence.…I am Deepak Rambarki, a Data Engineer with 5+ years of experience building cloud-native data platforms and real-time pipelines. I specialize in distributed systems, including Spark, Kafka, and Snowflake, and partner with finance and risk teams to deliver scalable, reliable data solutions that support regulatory reporting and business insights. I’m passionate about data quality, observability, and DataOps. I enjoy mentoring junior engineers and collaborating with cross-functional teams to translate complex requirements into robust architectures that improve time-to-insight and operational excellence.

Deepak Rambarki

Data Scientist, Data Analyst, Full Stack Developer, +4

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I am Deepak Rambarki, a Data Engineer with 5+ years of experience building cloud-native data platforms and real-time pipelines. I specialize in distributed systems, including Spark, Kafka, and Snowflake, and partner with finance and risk teams to deliver scalable, reliable data solutions that support regulatory reporting and business insights. I’m passionate about data quality, observability, and DataOps. I enjoy mentoring junior engineers and collaborating with cross-functional teams to translate complex requirements into robust architectures that improve time-to-insight and operational excellence.…I am Deepak Rambarki, a Data Engineer with 5+ years of experience building cloud-native data platforms and real-time pipelines. I specialize in distributed systems, including Spark, Kafka, and Snowflake, and partner with finance and risk teams to deliver scalable, reliable data solutions that support regulatory reporting and business insights. I’m passionate about data quality, observability, and DataOps. I enjoy mentoring junior engineers and collaborating with cross-functional teams to translate complex requirements into robust architectures that improve time-to-insight and operational excellence.

Available to hire

I am Deepak Rambarki, a Data Engineer with 5+ years of experience building cloud-native data platforms and real-time pipelines. I specialize in distributed systems, including Spark, Kafka, and Snowflake, and partner with finance and risk teams to deliver scalable, reliable data solutions that support regulatory reporting and business insights.

I’m passionate about data quality, observability, and DataOps. I enjoy mentoring junior engineers and collaborating with cross-functional teams to translate complex requirements into robust architectures that improve time-to-insight and operational excellence.

Skills

Experience Level

Expert

Expert

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Expert

Language

English

Fluent

Work Experience

Data Engineer at Charles Schwab

December 1, 2024 - October 31, 2025

Architected and deployed 12+ real-time data pipelines using Kafka and Spark Structured Streaming, reducing market data latency to under 2 seconds across equities and options. Led migration of 30TB+ legacy trading data to AWS S3 and Redshift using Glue and parallel load scripts, reducing query latency by 40% and lowering infrastructure costs by 25%. Developed automated ETL pipelines in Apache Airflow with SLA monitoring and retry logic, increasing on-time delivery across 10+ risk and compliance reports. Designed dimensional star schemas in Power BI using DAX and Snowflake models for near real-time reports used by 100+ financial advisors. Implemented fine-grained access control with AWS IAM roles and automated audit logging to ensure SOX and GDPR compliance. Established centralized data cataloging with versioned S3 datasets and schema registry, reducing duplication by 70%. Spearheaded data observability with lineage tracking, anomaly detection, and alerts, improving incident resolution t

Data Engineer at Adons Softech

July 1, 2023 - July 1, 2023

Built scalable ETL pipelines using PySpark and Talend across 5 client environments, integrating 40+ data sources (e.g., Salesforce, Oracle, S3) into Snowflake for unified analytics. Optimized legacy batch jobs with Spark parallelism and partition tuning, reducing daily processing time by 60% for 200M+ records. Provisioned AWS infrastructure with Terraform and GitLab CI/CD, achieving 95% reduction in deployment errors and consistent envs across dev/prod. Implemented RBAC in Redshift and Snowflake aligned with ISO 27001, improving auditability. Facilitated weekly client workshops to define SLAs, transformation rules, and 20+ KPIs for dashboards across healthcare, fintech, and logistics. Implemented incremental ETL loads to cut data latency in executive dashboards from 6 hours to under 30 minutes. Automated data quality checks with Great Expectations and Python, achieving 98% accuracy across 50+ pipelines. Containerized ETL workloads with Docker and Kubernetes, boosting scalability under

Data Analyst at Cybage Software

December 1, 2020 - December 1, 2020

Analyzed clickstream data from 15+ e-commerce platforms using SQL/Python, applying funnel and cohort analysis to identify drop-off points and increase conversion rates by 12%. Built dynamic Tableau dashboards to visualize customer behavior by region for 6 APAC/NA units. Automated Excel-based reporting with Python and Power BI templates, reducing manual workload by 8 hours weekly. Created automated SQL validations to detect upstream anomalies in orders, reducing errors during rollout. Documented reporting logic and KPIs with engineering/QA to streamline stakeholder communication. Introduced A/B testing for product recommendations, delivering a 9% lift in average order value. Standardized data dictionary to reduce reporting inconsistencies by 30%. Optimized MySQL queries for large datasets, cutting runtime from 15 minutes to under 2 minutes. Collaborated on forecasting models in Excel/Python for revenue planning across retail categories.