Senior Data Engineer with 8 Years Experience

Chakradhar Obulareddy

Email: **************@*****.***

Mobile: 469-***-****

LinkedIn: www.linkedin.com/in/chakradhar-reddy-38632688 Senior Data Engineer

PROFESSIONAL SUMMARY

Data Engineer with 8 years of experience designing, building, and optimizing reliable data platforms and pipelines for analytics, product, and machine learning use cases across cloud environments

Proven track record delivering batch and streaming pipelines using SQL, Python, Spark, and Kafka on platforms such as Snowflake, Databricks, BigQuery, and Redshift to improve data availability and decision making

Strong background in modern data stack tooling including dbt, Airflow, Fivetran, Azure Data Factory, and CI/CD with Git, enabling reproducible, well tested, and observable data workflows

Deep experience with data modeling, Lakehouse and medallion architectures, dimensional modeling, and data governance to improve data quality, compliance, and self service analytics

Collaborative partner to product, analytics, and data science teams with experience in retail, fintech, healthcare, media, and SaaS domains, translating business requirements into scalable data solutions

Facilitated excellent written oral communication skills to enhance team collaboration and streamline project updates.

Demonstrated passion for automation continual process improvement by implementing systems that increased efficiency by 30%.

TECHNICAL SKILLS

Programming Languages - Python, SQL, Scala, Java, Shell, Perl

Cloud And Data Platforms - AWS, Azure, GCP, Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, Delta Lake, Lakehouse, Azure Data Lake, Fabric

Data Engineering And Big Data - Spark, Kafka, Hadoop, Hive, dbt, Apache Beam, Kinesis, Airflow, Azure Data Factory, Fivetran, Informatica, REST APIs, microservices based data integrations

Orchestration, Devops, And Observability - Airflow, CI CD, Git, GitHub, Gitlab, Jenkins, CircleCI, Kubernetes, Docker, Terraform, monitoring and observability for data pipelines

Databases And Storage - PostgreSQL, MySQL, MongoDB, Cassandra, SQL Server, NoSQL stores, S3, Azure Data Lake Storage, GCS, Delta Lake, star schema, dimensional modeling, Oracle, Oracle Exadata

Analytics And Bi Tools - Tableau, Power BI, Looker, Sigma, data discovery, semantic modeling for self service analytics

Data Management And Governance - Data quality frameworks, data catalog, data lineage, data governance, data mesh concepts, PII protection, RBAC, GDPR compliance, HIPAA compliance

System Administration And Infrastructure - Linux-based processes, Unix file systems PROFESSIONAL EXPERIENCE

Capital One Aug 2025 – Present

Sr. Data Engineer

Built and productionized Python-based data quality monitoring frameworks covering 100+ HR metrics across 10M+ employee records, improving data accuracy and reducing executive reporting defects by 35%.

Implemented pre-load ELT validation in Snowflake using SQL to enforce primary key uniqueness and non-null constraints on the merged Emp_ID field, detecting and remediating 99.9% of cross-system reconciliation issues post-merger.

Architected the data foundation for workforce synergy and retention modeling, integrating Databricks with MLflow to support multiple predictive models used in post-merger headcount and attrition planning.

Delivered 15+ standardized Tableau dashboards directly sourced from optimized Snowflake tables, enabling near real-time less than15 min latency executive visibility into attrition, diversity, compensation, and staffing changes.

Led the integration of Discover’s core HR systems into Capital One’s People Data Platform, consolidating 20+ source tables and harmonizing multiple HR schemas into a unified enterprise model.

Re-architected ETL pipelines using Snowflake columnar storage, clustering keys, and pruning, reducing Tableau dashboard refresh times by 45% and improving query scan efficiency by 40%.

Developed and optimized Shell and Perl scripts to automate Unix file systems and Linux-based processes, reducing manual intervention by 40% and enhancing system reliability.

Managed Oracle and Oracle Exadata environments, implementing data warehousing solutions that improved data retrieval speeds by 30% and supported scalable data flows. United HealthGroup May 2023 – Aug 2025

Data Engineer

Architected and deployed scalable data pipelines using PySpark processing over 50 TB of healthcare data daily, improving batch job efficiency by 35% through optimized partitioning, bucketing, and indexing strategies.

Migrated legacy healthcare data platform from on-premises to Microsoft Azure, leveraging Virtual Machines, ADLS, and HDInsight, reducing infrastructure costs by 40% while increasing system scalability and availability for clinical reporting and dashboards.

Enhanced Snowflake query performance by up to 60% using clustering, materialized views, and search optimization, supporting mission-critical population health dashboards and risk stratification models.

Led cloud migration of legacy on-premises data systems to Azure using Azure Database Migration Service (DMS), Azure Blob Storage, and Azure Databricks, improving data availability, compliance, and cost efficiency.

Tuned Azure Synapse Analytics and Azure SQL Database for healthcare analytics workloads, optimizing queries and data models for HEDIS measures, utilization reports, and population health dashboards.

Analyzed Delta tables performance based on Query Execution plans and chose optimal Partition Strategy to avoid small file problems and used OPTIMIZE and Z-Ordering to improve the performance.

Utilized Agile methodology to drive system/architecture improvements, enhancing team productivity by 25% and delivering projects ahead of schedule.

Streamlined load/extract processes using standard tools and scripts, achieving a 20% reduction in data processing time and ensuring data integrity.

Amazon Sep 2022 – May 2023

Data Engineer

Designed and owned end-to-end measurement pipelines for Sponsored Display within ADS, processing billions of daily ad events (impressions, clicks, spend, conversions) using AWS Glue, EMR (Spark), and S3, delivering sub- hour metric availability for advertiser reporting and optimization.

Partnered closely with MADS Scientists, Product, and Finance teams to define, validate, and productionize metric logic, attribution models, and deduplication rules, ensuring consistency across experimentation, reporting, and billing systems.

Implemented scalable attribution pipelines supporting click-through and view-through conversions for Sponsored Display and Sponsored Brands, correctly assigning credit across multi-touch customer journeys.

Optimized large-scale Spark jobs on Amazon EMR, leveraging partitioning, broadcast joins, and incremental processing, reducing pipeline latency by 40% and supporting traffic spikes during Prime Day and holiday events.

Drove AWS cost optimization initiatives, reducing compute and storage costs by 20% through EMR right-sizing, Glue job tuning, S3 lifecycle policies, and query optimization.

Designed and implemented environment setup, mount types, and permissions protocols, increasing operational efficiency by 15% and minimizing security risks.

Demonstrated excellent written and oral communication skills to facilitate cross-functional collaboration, leading to a 10% increase in project success rate.

Archer Daniel Midlands Oct 2018 – Dec 2020

Data Engineer

Analyzed and curated large-scale agricultural, commodity trading, and supply chain datasets to support pricing, margin analysis, inventory optimization, and demand planning for global operations teams.

Built and maintained interactive Tableau dashboards connected to Snowflake and AWS S3-backed datasets, enabling leadership to track commodity price movements, inventory turnover, and profitability KPIs in near real time.

Developed SQL-based analytical models in Snowflake, transforming raw ingestion data into business-ready fact and dimension tables for consistent reporting across teams.

Performed data validation, reconciliation, and anomaly analysis across procurement, logistics, and financial datasets, reducing reporting discrepancies by 25%.

Supported AWS-based data ingestion pipelines (S3, Glue, EMR) by validating outputs and collaborating with data engineering teams to ensure analytics readiness.

Exhibited passion for automation and continual process improvement, resulting in a 35% reduction in operational costs and enhanced workflow efficiency.

Dow Chemicals Sep 2016 – Oct 2018

Data Engineer

Developed and maintained ETL pipelines to ingest, transform, and integrate manufacturing, supply chain, and financial data, supporting analytics across multiple global business units.

Built SQL-based data models and reporting tables used by operations and finance teams to track production efficiency, plant utilization, and cost performance, improving reporting accuracy and consistency.

Automated recurring data preparation and validation processes using Python and SQL, reducing manual reporting effort by 30% and improving data reliability for monthly and quarterly reviews.

Optimized legacy database queries and batch jobs, reducing report generation time by 25% and improving system stability for business-critical analytics.

Collaborated with IT, data governance, and security teams to ensure compliance with internal data standards and access controls for sensitive operational and financial data.

Worked in an Agile delivery model, participating in sprint planning, backlog grooming, and cross-functional reviews to deliver incremental data improvements.

Leveraged toolsets and pipes to optimize data flows and automate routine tasks, significantly reducing processing errors and improving system performance.

CERTIFICATIONS

Databricks Certified Data Engineer Professional

SnowPro Core Certification

Microsoft Certified Azure Data Engineer Associate EDUCATION

Masters in Business Analytics - University of North Texas

Bachelor's in Electronics and Communications Engineer - Visvesvaraya Technological University

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