Senior Data Engineer with Cloud Expertise

Chennupati Ravi

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

Mobile: 708-***-****

LinkedIn: http://www.linkedin.com/in/ravikdata99

Senior Data Engineer

PROFESSIONAL SUMMARY

Versatile Senior Data Engineer with 5 + years of experience architecting and deploying scalable, high-performance data infrastructures across cloud platforms and enterprise analytics environments.

Expert in designing and building end-to-end data pipelines using modern frameworks and tools, enabling seamless data ingestion, transformation, and warehousing to support predictive analytics and business intelligence.

Strong proficiency in multi-cloud environments (AWS, Azure, GCP), leveraging big data technologies such as Apache Spark, Kafka, and data lakehouse architectures to manage and process large volumes of structured and semi-structured data.

Skilled in data modeling, metadata management, and data governance, including ensuring data integrity, lineage, and compliance with regulatory standards, thereby enabling trustworthy and actionable insights.

Adept at collaborating cross-functionally with data scientists, product owners, and business stakeholders to translate requirements into scalable data solutions, drive actionable metrics, and support data-driven decision-making.

Committed to continuous improvement, implementing MLOps/DevOps practices—CI/CD, containerization, infrastructure as code—to operationalize data pipelines, reduce time to insight, and improve reliability of production analytics systems.

Facilitated excellent written oral communication skills to enhance collaboration and streamline cross-departmental projects.

Implemented passion automation continual process improvement, resulting in a 30% increase in operational efficiency.

TECHNICAL SKILLS

Programming & Scripting Languages - Python, SQL, Scala, Java, Bash, Shell, Perl

Big Data Frameworks & Processing - Apache Spark, PySpark, Hadoop, Hive, Flink, Kafka, Beam, Snowflake, Delta Lake, Databricks

Data Pipelines & Orchestration - Apache Airflow, Azure Data Factory (ADF), AWS Glue, Cloud Composer, Dataflow, NiFi, CI/CD Pipelines, ETL

Cloud Platforms - Amazon Web Services (S3, Redshift, Glue, Lambda, EMR, Kinesis) Microsoft Azure (Synapse, Data Lake Gen2, Databricks, Event Hub, Fabric) Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Dataproc)

Data Warehousing & Storage - Azure Synapse Analytics, Amazon Redshift, Google BigQuery, Snowflake, Teradata, PostgreSQL, MySQL, MongoDB, Oracle, Oracle Exadata

Data Modeling & Architecture - Dimensional Modeling, Star/Snowflake Schema, Data Lakehouse Architecture, Metadata Management, Partitioning, Indexing

Devops & Automation Tools - Docker, Kubernetes, Terraform, Jenkins, Git, CloudFormation, CI/CD Pipelines, Infrastructure as Code (IaC)

Data Governance & Security - Azure Purview, AWS Lake Formation, GCP Data Catalog, IAM, RBAC, Encryption (KMS), Data Lineage & Compliance (GDPR, HIPAA)

Analytics & Bi Tools - Power BI, Tableau, Looker Studio, Databricks SQL Analytics

Monitoring & Logging - CloudWatch, Azure Monitor, Stackdriver, ELK Stack, Prometheus, Grafana

Collaboration & Methodologies - JIRA, Confluence, Agile/Scrum, DevOps Practices

Data Integration Tools - Informatica

System Administration - Linux, Unix

PROFESSIONAL EXPERIENCE

Fidelity Investments Sep 2024 – Present

Senior Data Engineer

Developed and optimized large-scale ingestion pipelines in Azure Data Factory (ADF) and Azure Databricks

(PySpark, SQL), capturing daily trade, investment and account-level data for analytics, reducing data latency by 35%.

Architected and maintained a cloud-native data lakehouse on Azure Data Lake Storage Gen2 using Delta Lake, implementing partitioned data layers and enabling scalable analytics across structured and semi-structured financial datasets.

Built high-performance analytic models and dashboards using Azure Synapse Analytics and Power BI, creating interactive dashboards that visualized client portfolio trends, product usage, and cost metrics for business stakeholders.

Implemented rigorous data governance, lineage and security frameworks using Azure Purview, Key Vault, and RBAC, ensuring adherence to financial services compliance standards (SOX/FFIEC) and improving audit readiness across data platforms.

Automated deployment workflows and infrastructure provisioning using Azure DevOps, Terraform, Docker, and Kubernetes, enabling continuous integration and delivery of data services and achieving 99.9% uptime in production.

Partnered with data scientists, product owners and architects to define analytics requirements and develop feature- stores using PySpark, SQL Server, and MongoDB, thereby enhancing model reuse and reducing development time for new data products.

Developed Shell and Perl scripts to automate ETL/database load/extract processes, increasing data processing efficiency by 40% and reducing manual errors.

Implemented Oracle and Oracle Exadata solutions to enhance backend focus and system/architecture improvements, resulting in a 50% increase in database query performance. Apple May 2023 – Aug 2024

Senior Data Engineer

Built and managed large-scale data warehouse platforms on AWS Redshift and S3, ingesting multi-petabyte telemetry and device usage data, enabling ad-hoc analytics and reducing query latency by 50%.

Engineered real-time and batch data pipelines using AWS Glue, EMR (Spark), and Kinesis, to process heterogeneous device, iOS / macOS service logs and structured financial data for analytics across Apple services.

Designed and implemented dimensional-model schemas and metadata layers in PostgreSQL and Snowflake, while also integrating MongoDB and DynamoDB for semi-structured data, thereby enhancing data accessibility for analytics and ML teams.

Deployed CI/CD-driven infrastructure using Terraform, Docker containers and Kubernetes, along with Jenkins and Git pipelines, yielding a 30 % faster production rollout and 99.9 % system availability.

Established robust data governance & security frameworks leveraging AWS IAM, KMS encryption, and Lake Formation for access control, encryption, and lineage tracking, ensuring internal audit and privacy-compliance readiness.

Led cross-functional collaborations with product owners, data scientists and engineering peers in an Agile/Scrum environment, translating device-service insights into analytics delivery using Python, SQL, and AWS-native services across the stack.

Utilized Linux and Unix environments to optimize Unix file systems, mount types, and permissions, improving data security and access speed by 25%.

Created Linux-based toolsets and scripts to streamline data flows and processes, reducing operational costs by 30% through automation.

McKesson Apr 2022 – Mar 2023

Data Engineer

Engineered and maintained automated ingestion pipelines using Azure Data Factory (ADF) and Databricks

(PySpark/SQL) to consolidate pharmacy, logistics, and claims datasets, improving latency by 32% and enabling more timely analytics.

Designed a scalable lakehouse architecture on Azure Data Lake Storage Gen2 with Delta Lake, implementing bronze–silver–gold layering and enabling unified handling of structured inventory data and semi-structured sensor logs.

Developed and optimized analytics models and dashboards in Azure Synapse Analytics and Power BI, creating insights into supply-chain performance, order fulfilment, and cost variances for executive decision-makers.

Applied rigorous data governance and security practices, using Azure Purview, RBAC, and Key Vault, ensuring full compliance with healthcare data regulations including HIPAA and internal audit standards.

Built repeatable CI/CD pipelines with Azure DevOps, Terraform, Docker, and Kubernetes to deploy data pipelines reliably, achieving 99.9% uptime and reducing manual deployment overhead by 45%.

Partnered with data scientists and business stakeholders in an Agile/Scrum environment, translating supply-chain optimization and pharmacy operations requirements into reusable data services, feature stores, and analytics assets.

Leveraged Informatica and ETL to design robust data integration workflows, ensuring seamless data transfer and improving data accuracy by 35%.

Demonstrated excellent written and oral communication skills to convey technical concepts effectively, enhancing team collaboration and project alignment.

Walmart Jun 2019 – Jul 2021

Data Engineer

Developed and scaled batch and streaming data pipelines on Apache Beam and Apache Spark (via Google Cloud Dataflow and Google Cloud Dataproc) to ingest retail transaction and click-stream data into BigQuery, improving data freshness and analytics throughput by 50%.

Architected and maintained a lakehouse environment on Google Cloud Storage and BigQuery, implementing partitioning, clustering, and schema evolution to support real-time dashboards and self-service analytics for inventory and supply-chain teams.

Built and optimized data models, views, and metrics layers in BigQuery and integrated with visualization tools like Looker Studio and Power BI, enabling actionable insights into customer behavior, sales trends, and omnichannel performance.

Implemented data governance and monitoring frameworks using Google Cloud Data Catalog, IAM roles and audit logging to ensure data quality, lineage, and compliance in accordance with enterprise standards around retail operations.

Automated deployment and orchestration of data workflows using Apache Airflow (via Cloud Composer), CI/CD pipelines with Terraform and Git, and containerization with Docker/Kubernetes to improve deployment frequency and system reliability.

Collaborated with cross-functional teams (data scientists, product managers, UX engineers) to deliver scalable analytics products, feature stores, and ML-ready data sets—translating complex retail requirements into engineering solutions and reducing time-to-insight for new business initiatives.

Showcased passion for automation and continual process improvement by implementing standard tools and pipes, leading to a 20% reduction in processing time.

EDUCATION

Masters in Computer Science - Texas Tech University

Bachelors in Computer Science - Lovely Professional University

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