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Santhosh Rangu - Data Engineer

[email protected]

Location: Richmond, Virginia, USA

Relocation:

Visa: H1B

Resume file: Santhosh-Resume_1793033280690.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

PROFESSIONAL SUMMARY Data & AI Engineer with 7+ years of experience building scalable data platforms, cloud data pipelines, Big Data solutions, machine learning infrastructure, and AI/GenAI applications. Strong hands-on expertise in Python, PySpark, Databricks, SQL, AWS, Azure, GCP, Spark and enterprise data engineering.Hands-on experience building ML pipelines, feature engineering workflows, MLflow-based ML lifecycle solutions, and model deployment pipelines. Experienced in developing modern Generative AI solutions using OpenAI, experience in designing AI-ready data platforms that ingest, transform, enrich, and serve structured and unstructured enterprise data for machine learning and Generative AI applications. Experienced with document processing, embedding generation, semantic search, vector retrieval, RAG pipelines, and LLM application integration. Cloud experience across AWS, Microsoft Azure and GCP with strong knowledge of scalable data-processing architectures and production data platforms. Developed executive-level dashboards and KPI reports to provide visibility into business performance, operational trends, and progress against strategic goals. CORE COMPETENCIES Languages: Python, Async Python(asyncio), SQL, Bash Data Engineering: Apache Airflow, dbt, Apache Spark, Databricks,Pandas, NumPy Cloud & Databases: AWS, Microsoft Azure, GCP, DB2, Snowflake, PostgreSQL, Google BigQuery, AWS Redshift, Azure Synapse Machine Learning / AI: ML Pipelines, MLflow, PyTorch, LangChain, LlamaIndex, scikit-learn, RAG pipelines, drift monitoring APIs & AI Integration: FastAPI, FastMCP, REST APIs, LLM/MCP integration DevOps & Governance: Git, GitHub Actions, GitLab CI/CD, Docker, Terraform, Kubernetes, data lineage, data cataloging Business Intelligence & Visualization: Looker, Sigma, Omni, Power BI, Tableau and Looker Studio. PROFESSIONAL EXPERIENCE Senior Data Engineer Client: BLUE CROSS BLUE SHIELD ASSOCIATION (BCBSA) - Virginia Beach, VA (Remote) | Jun 2021 - Present Description: The project delivers benefits, plans and incentives to federal employees through scalable, secure data pipelines. It integrates data from multiple partners using batch and real-time processing, applies business rules to validate, transform, and enrich the data, and delivers reliable outputs to end users and partner systems. The pipelines also support analytics and downstream machine learning and generative AI applications. Responsibilities: Design and develop scalable Python, PySpark, and Databricks data pipelines for high-volume enterprise data processing. Build batch and real-time data pipelines for ingesting, transforming, validating, enriching, and delivering structured and semi-structured data. Developed reusable Python modules and class-based components for data ingestion, transformation, validation, and delivery. Applied object-oriented design principles to organize pipeline logic, improve code reuse, and simplify maintenance. Built Python utilities to automate data-quality checks, reconciliation, and operational reporting. Used Python s asyncio and asynchronous I/O to run independent API and data-source requests concurrently. Wrote unit and integration tests with pytest for Python pipeline components, APIs, and data transformations. Containerized Python data-processing services with Docker to provide consistent development, testing, and deployment environments. Developed FastAPI endpoints to trigger data workflows and provide pipeline status, validation results, or curated data to downstream applications. Connected MCP tools to data retrieval and search services, with input validation and controlled responses for downstream AI applications. Built FastMCP tools and resources that let LLM applications securely access approved enterprise data and RAG workflows. Develop and support model deployment pipelines for production machine learning applications. Build AI-ready data pipelines for structured and unstructured data used by downstream ML and Generative AI workloads. Work with Large Language Models (LLMs) to develop AI-powered applications and data workflows. Design and implement RAG (Retrieval-Augmented Generation) pipelines connecting enterprise data sources with LLM applications. Supported analysis of Annual Recurring Revenue (ARR), gross margin, pipeline coverage and sales funnel performance. Supported dashboard performance optimization, data refresh validation, and reconciliation between dashboard outputs and underlying warehouse data. Data Engineer Client: SUNTRUST - Richmond, VA | Jan 2019 - Jun 2021 Description: Supported the SunTrust-BB&T merger by helping integrate BB&T financial data into SunTrust s Hadoop environment. Worked on the Bulk Data Extract (BDE) application for Black Knight data, bringing files into the shared cluster while preserving prior versions in BDE and retaining the latest versions in Black Knight. This work supported the consolidation of both banks data on a single platform. Responsibilities: Supported the SunTrust-BB&T merger by integrating BB&T customer and financial data into SunTrust s Hadoop environment. Developed Bulk Data Extract (BDE) workflows for Black Knight data, bringing files into the shared Hadoop cluster while retaining prior versions in BDE. Supported data-version handling between BDE and Black Knight, where BDE retained historical files and Black Knight held the latest versions. Built Python, PySpark, and Spark workflows to transform and migrate customer data mart data to Big Data platforms. Created Kafka streaming pipelines to consume real-time messages and land JSON data in Hadoop. Developed Ab Initio workflows to ingest and transform source data before loading Teradata dimension and fact tables. Supported both batch and streaming data integration as part of the effort to consolidate the banks data on a single platform. ETL Developer Client: Fannie Mae - Herndon, VA | Mar 2018 - Dec 2018 Description: Fannie Mae is one of the biggest Mortgage companies in the United States. I am part of Multifamily RDW project. The project is to simplify the multifamily RDW project based on business requirements and existing code using the technical platforms Ab-Initio, SQL for Oracle database on UNIX. Responsibilities: Involved in software development life cycle by creating the high-level design to the detailed level design of the project. Define the dataflow requirements, interpret the transformation rules for all target data objects and develop graphs to support the transformation, document technical specifications. Used ETL tool Ab Initio to pull data from source systems, cleanse, transform, and load data into Oracle database. Worked closely with the business team to get clear idea about the requirements. Performed complex queries involving large data volumes. Replicate operational tables, Transform and load data into Operational Data Store using Ab Initio GDE. Development of complicated graphs with various Ab Initio components such as Join, Rollup, Lookup, Gather, Merge, sort and Dedup sorted. Deploy, test and run the Ab Initio graph as executable UNIX shell scripts. Modified the Ab Initio component parameters, utilize data parallelism and thereby improve the overall performance to fine-tune the execution times. Worked on webservices XML, JSON, Restful and Soap. Keywords: continuous integration continuous deployment artificial intelligence machine learning access management business intelligence information technology Virginia