Senior Data Engineer with Cloud & ETL Expertise

Krishnasri Ramineni

Denton, TX *******************@*****.*** +1-940-***-**** https://www.linkedin.com/in/krishnasri-ramineni/ PROFESSIONAL SUMMARY

Senior Data Engineer with 6+ years of hands-on experience building scalable data pipelines, cloud data platforms, and analytics infrastructure across Azure, AWS, and GCP. Worked across healthcare, financial services, and retail industries delivering production-grade ETL/ELT systems that process millions of records daily. Strong background in PySpark, Databricks, Delta Lake, Kafka, Airflow, and Snowflake with solid expertise in data modeling, streaming architectures, and warehouse optimization. Have worked on both large-scale batch systems handling millions of records daily and streaming setups where data needs to move fast. Experienced working alongside data science and ML teams to build reliable feature datasets and data foundations supporting model training, forecasting, and real-time reporting. Strong on governance and security: access controls, lineage tracking, and keeping pipelines compliant with financial and healthcare regulations. Enjoy working through performance problems, cutting down job runtimes, and debugging production failures that don't show up in test environments. TECHNICAL SKILLS

Cloud Platforms: Azure (ADF, Databricks, Synapse Analytics, ADLS Gen2, Blob Storage) AWS (S3, Glue, EMR, Redshift, Lambda) GCP (BigQuery, Cloud Storage) Languages & Processing: Python, SQL, PySpark, Spark SQL, Scala, T - SQL Data Engineering: ETL/ELT, Delta Lake, Apache Kafka, Apache Airflow, dbt, Spark Structured Streaming, REST APIs Warehousing & Modeling: Snowflake, Azure Synapse, BigQuery, Redshift, Star/Snowflake Schema, Dimensional Modeling AI / ML Tooling: Databricks Feature Store, MLflow, Pandas, ML pipeline integration, LLM-ready data pipelines Databases: SQL Server, PostgreSQL, Oracle, MySQL, MongoDB, Cassandra DevOps & CI/CD: Git, Azure DevOps, GitHub Actions, Jenkins, Docker, Terraform Analytics & BI: Power BI, Tableau, Looker

Governance & Security: Unity Catalog, RBAC, IAM, Data Lineage, Azure Monitor, CloudWatch, ADLS ACLs PROFESSIONAL EXPERIENCE

Walgreens – Senior Data Engineer Oct 2024 – Present Chicago, Illinois

• Designed and maintained end-to-end ADF-orchestrated ETL/ELT pipelines across pharmacy, retail, and healthcare business units, processing over 1TB of data daily on Databricks; improved Spark job throughput by 35% through partition tuning, broadcast join optimization, caching strategies, and cluster right-sizing.

• Architected ADLS Gen2 medallion storage layers (raw, curated, analytics) with Unity Catalog access controls, column-level security, row-level filters, and automated data lineage tracking to meet enterprise governance and HIPAA-adjacent compliance requirements.

• Designed star-schema fact and dimension tables in Azure Synapse Analytics feeding Power BI dashboards used by 5+ business units for KPI tracking and operational reporting; cut average dashboard refresh time by 25% through incremental load patterns and partition pruning.

• Partnered with data science teams to design and maintain curated feature datasets in Databricks Feature Store, supporting demand forecasting and inventory optimization models; built reusable PySpark transformation pipelines that standardized feature delivery and reduced ad-hoc data prep work for the ML team.

• Integrated MLflow experiment tracking into Databricks workflows, logging pipeline run metadata, feature versions, and model input schemas so the ML team could trace exactly which data went into each training job and reproduce results consistently.

• Built Databricks Structured Streaming jobs consuming from Kafka topics to reduce data latency across operational pipelines, enabling merchandising and supply chain teams to act on near real-time KPI data instead of waiting for overnight batch loads.

• Implemented layered data quality checks in Python and SQL covering null detection, schema drift alerts, referential integrity validation, and row-count reconciliation between source and target; integrated checks into pipeline runs so failures block downstream loads rather than silently propagating bad data.

• Worked with platform, security, and business stakeholders to define and enforce data contracts and SLAs across 10+ pipeline feeds; contributed to Azure cost optimization by identifying over-provisioned clusters and migrating low-priority workloads to spot instances, reducing monthly compute spend. Wells Fargo – Data Engineer Jan 2021 – June 2023 Hyderabad, India

• Built scalable Azure Data Factory ingestion pipelines pulling high-volume transactional and customer data from 15+ source systems into Azure Data Lake; optimized Databricks Spark jobs processing credit, loan, and risk datasets across multiple product lines, cutting average processing time by 40% through query rewrites and partition strategy improvements.

• Developed Kafka streaming pipelines and Python REST APIs that fed transaction events into real-time fraud detection and monitoring systems; worked with the risk team to tune topic partition counts and consumer group configurations to handle peak transaction volumes without lag.

• Built enterprise data quality frameworks covering schema validation, source-to-target reconciliation, null and duplicate detection, and automated threshold alerting; frameworks were used to validate datasets feeding SOX and PCI regulatory reports, reducing compliance-related data rework.

• Led migration of legacy Informatica workflows and stored-procedure-heavy SQL pipelines to Azure cloud-native architecture; reduced infrastructure costs by 20%, improved pipeline maintainability, and documented migration patterns adopted by other teams.

• Enforced enterprise data security controls including RBAC role assignments, ADLS ACLs, and column-level masking on PII and financial fields; worked with the security team to align access patterns with internal policy and external regulatory requirements.

• Built and maintained Azure DevOps CI/CD pipelines for all data pipeline releases, covering unit testing, data validation, environment promotion, and automated rollback on failure; reduced deployment errors and cut release cycle time.

• Contributed to cloud modernization planning by profiling on-prem SQL Server batch workloads and migrating high-priority jobs to Databricks; documented compute savings, improved monitoring coverage, and helped the team establish standards for new cloud-native pipeline development. Best Buy – Data Engineer Jan 2019 – Dec 2020 Bangalore, India

• Designed and maintained Python/PySpark batch pipelines on AWS ingesting and transforming high-volume e-commerce transaction records and clickstream events; delivered clean, analytics-ready datasets to marketing and personalization teams on daily and hourly schedules.

• Built Kafka-based streaming pipelines to capture cart, browse, and click events in near real-time and route them to downstream recommendation and analytics systems; worked with the product team to define event schemas and ensure consistent payload structure across web and mobile sources.

• Migrated on-prem ETL processes to cloud-native AWS architecture using S3, Glue, and EMR; improved pipeline fault tolerance by 35%, reduced infrastructure maintenance burden, and eliminated overnight batch window constraints that had been limiting data freshness.

• Developed a shared ingestion and transformation library in PySpark that standardized how teams connected to source systems, applied common transforms, and wrote output datasets; adopted by four teams and cut the time to build new pipelines from days to hours.

• Optimized Spark SQL and T-SQL queries for inventory reconciliation and weekly sales reporting workflows; profiled execution plans, rewrote inefficient joins, and added result caching, reducing job runtimes and freeing up cluster capacity for other workloads.

• Set up CloudWatch dashboards and SNS alerting for pipeline health monitoring, tracking job durations, failure rates, and data volume anomalies; enabled the team to catch and resolve issues before they surfaced in analyst reports or caused SLA breaches.

• Collaborated with BI and analytics teams throughout the data model design process to ensure table structures and grain definitions matched their reporting needs; reduced downstream rework and cut the volume of ad-hoc data fix requests from analysts.

EDUCATION

Master of Science (M.S.) – Information Technology Leadership Aug 2025 Indiana Wesleyan University

CERTIFICATIONS

Microsoft Certified: Azure Data Engineer Associate (DP-203)