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Vetted Batch Processing Professionals
Pre-screened and vetted.
Nikitha Margadi
Mid-level Data Engineer specializing in cloud lakehouse, streaming, and MLOps
“Data engineer at AT&T focused on large-scale telecom (5G/IoT) data platforms, owning end-to-end pipelines from Kafka/Azure ingestion through Databricks/Delta Lake transformations to serving analytics and ML. Has operated at very high volumes (~50+ TB/day) and delivered measurable performance gains (25–30% faster processing) plus improved reliability via Airflow monitoring, robust data quality checks, and resilient external data collection patterns (rate limiting, retries, dynamic schemas).”
Krishnamraju Penumatsa
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
“Data engineer currently at American Airlines who built and owned end-to-end flight operations and booking data pipelines (batch + real-time) using Azure Data Factory, Kafka, Spark/Databricks, Synapse, and Snowflake—processing hundreds of GBs/day. Strong focus on reliability and data quality (idempotency, checkpointing, retries, validation/alerts) and delivered near-real-time analytics powering Power BI dashboards; previously helped stand up an early-stage data platform at Sysco on AWS (Glue/S3/Redshift) with Airflow and Jenkins CI/CD.”
Yashwanth Reddy
Mid-Level Software Engineer specializing in backend microservices and cloud platforms
“Backend engineer in healthcare data systems who has owned production pipelines end-to-end, from ingesting patient and claims data to serving it through secure APIs. Brings a strong mix of Python, SQL, microservices, cloud deployment, and data reliability practices, with measurable performance gains and experience building resilient integrations with external data sources.”
Shanthi M
Senior .NET Full-Stack Developer specializing in Azure cloud and microservices
“Backend/data engineer with hands-on production experience building reliable Python FastAPI services on Kubernetes and delivering AWS EKS + Terraform CI/CD with strong secrets isolation and rollback practices. Also built AWS Glue ETL pipelines into S3/Redshift with schema-evolution handling and data-quality controls, modernized legacy analytics into modular Python services with parallel-run parity validation, and has demonstrated SQL tuning impact (minutes to seconds) plus ownership of batch pipeline incidents end-to-end.”
Mid-level Data Engineer specializing in financial risk, compliance, and real-time streaming
Mid-level Data Engineer specializing in cloud ETL and data platforms (AWS/Azure)
Mid-level Software Engineer specializing in cloud-native backend systems (GCP/Azure)
Mid-level Full-Stack Software Developer specializing in cloud-native microservices
Mid-Level Data Engineer specializing in scalable cloud data pipelines and API-driven data services
Mid-level AI/ML Engineer specializing in RAG, LLMs, and MLOps for finance
Senior Full-Stack Java Developer specializing in cloud-native microservices
Mid-level Full-Stack Developer specializing in FinTech and cloud-native microservices
Mid-Level Backend/AI Software Engineer specializing in data pipelines and LLM integrations
Mid-level Data Engineer specializing in cloud ETL, Spark, and analytics platforms
Mid-level Software Engineer specializing in distributed systems and data platforms
Senior Software Engineer specializing in cloud-native microservices and data platforms
Mid-level Data Engineer specializing in cloud data platforms and real-time pipelines
Senior AI/ML Engineer specializing in healthcare AI and GenAI platforms
Intern Software Engineer specializing in AI/LLM, cloud, data, and robotics
Mid-level Backend Engineer specializing in cloud-native FinTech data platforms
Mid-Level Full-Stack Software Engineer specializing in APIs, microservices, and data pipelines
Senior Data Engineer specializing in cloud data platforms and real-time pipelines
Mid-level Full-Stack AI Engineer specializing in RAG systems and FinTech platforms
Staff Full-Stack Engineer specializing in cloud, data platforms, and GenAI systems