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Accomplished Senior Data Engineer with over a decade of expertise in data engineering and business intelligence, leveraging advanced skills in SQL, ETL, and data warehousing for innovative data engineering solutions. Excelled in architecting data solutions that enhanced operational efficiency and data-driven decision-making. Demonstrated leadership in transforming complex data insights into actionable strategies, showcasing exceptional analytical and team management skills.

18 years of experience primarily in Banking and Financial Services, with strong expertise in FR 2052a FED Regulatory Reporting (5G & 6G), Analytics, and Data Governance. Proven track record in the design, development, and implementation of Cloud Data Lake, Data Mesh, Data Warehouse, and Business Intelligence solutions. Possesses strong technical, analytical, and functional skills, with end-to-end ownership across SDLC and Agile methodologies, including requirements analysis, documentation, architecture design, development, testing, implementation, production support, incident management, root cause analysis, and real-time debugging. Designed and delivered cloud-native solutions with a strong focus on engineering excellence, automation, innovation, and cross-team collaboration. Continuously evaluated and adopted new technologies, tools, and working practices, demonstrating agility and a strong learning mindset. Brought a curious, questioning approach to solution design, contributing innovative ideas and improving architectural and operational outcomes. Triaged complex production issues, performed root cause analysis (RCA), and made decisive, data-driven decisions under high-pressure scenarios. Acted confidently in break-glass production incidents, restoring services and minimizing customer impact during critical outages. Designed and implemented scalable solutions using AWS services, including compute, storage, networking, containers, and serverless frameworks. Built and deployed applications using Infrastructure as Code (IaC) tools such as Cloud Formation, Terraform, Ansible, and ARM, ensuring repeatability and compliance. Applied object-oriented principles and design patterns to develop maintainable, extensible, and high-performing applications. Developed and deployed containerized applications, leveraging Docker and Kubernetes, with strong experience deploying workloads on Amazon EKS. Implemented CI/CD pipelines using Jenkins and Git-based workflows, enabling automated build, test, and deployment processes. Collaborated effectively with distributed teams and stakeholders, both in-person and virtually, building trusted and productive working relationships. Worked with enterprise database technologies including Oracle, PostgreSQL, and SQL Server to support transactional and analytical workloads. Defined and implemented cloud reliability, resiliency, and observability strategies, ensuring high availability and performance of Tier-3 critical systems. Established and enforced engineering standards and best practices, contributing to increased engineering maturity and operational stability. Deployed and managed LLM-based solutions, including Retrieval Augmented Generation (RAG) architectures in cloud environments. Built full-stack AI-powered applications, integrating LLMs with backend services and cloud infrastructure. Applied best practices in prompt engineering, data privacy, hallucination mitigation, bias awareness, token usage, and cost optimization. Demonstrated understanding of GenAI modalities, determinism vs. randomness, and agentic behavior in production systems. Developed AI workflows and services using object-oriented Python, ensuring scalability, reliability, and maintainability. Data engineering professional poised to add significant value through comprehensive experience in developing scalable data solutions. Noted for strong team collaboration and adaptability in fast-paced environments. Reliable in driving results with key skills in data modeling, ETL processes, and cloud-based data platforms. Experienced with building and maintaining data pipelines to ensure seamless data flow. Utilizes advanced knowledge of big data technologies to drive data-driven decision-making. Track record of enhancing data architecture for improved performance and reliability. Senior engineering professional with deep expertise in data architecture, pipeline development, and big data technologies. Proven track record in optimizing data workflows, enhancing system efficiency, and driving business intelligence initiatives. Strong collaborator, adaptable to evolving project demands, with focus on delivering impactful results through teamwork and innovation. Skilled in SQL, Python, Spark, and cloud platforms, with strategic approach to data management and problem-solving. Diligent [Desired Position] with robust background in data engineering and proven ability to design and implement complex data pipelines. Successfully contributed to optimizing data architecture and enhancing data processing efficiencies. Demonstrated expertise in big data technologies and proficiency in Python and SQL. Responsive expert experienced in monitoring database performance, troubleshooting issues and optimizing database environment. Possesses strong analytical skills, excellent problem-solving abilities, and deep understanding of database technologies and systems. Equally confident working independently and collaboratively as needed and utilizing excellent communication skills. Detail-oriented [Job Title] designs, develops and maintains highly scalable, secure and reliable data structures. Accustomed to working closely with system architects, software architects and design analysts to understand business or industry requirements to develop comprehensive data models. Proficient at developing database architectural strategies at the modeling, design and implementation stages. Astute [Job Title] with data-driven and technology-focused approach. Communicates clearly with stakeholders and builds consensus around well-founded models. Talented in writing applications and reformulating models.

Delivered scalable, high-performance data engineering solutions with over 11 years of IT expertise in Data Engineering, Data Warehousing, and Big Data. Leveraging 5+ years of experience in data engineering and 4+ years in data warehousing across healthcare, finance, retail, and manufacturing domains. Designed, built, and optimized large-scale ETL/ELT pipelines using Azure Data Factory, Azure Databricks, AWS Glue, Snowflake, and Apache Spark. Implemented real-time data ingestion and analytics pipelines leveraging Kafka, Spark Streaming, Azure Event Hubs, and AWS Kinesis. Developed reusable, parameterized pipelines to reduce development time and improve maintainability. Created optimized data models and schemas using dimensional modeling techniques (star schema, snowflake schema). Applied advanced performance tuning techniques such as partitioning, bucketing, indexing, caching, and workload management. Integrated structured, semi-structured, and unstructured data from diverse sources including RDBMS, APIs, and cloud storage. Automated ETL workflows using Apache Airflow, Oozie, Control-M, IBM Tivoli, Jenkins, and Azure DevOps pipelines. Utilized advanced Snowflake features including SnowSQL scripting, time travel, zero-copy cloning, and complex SQL functions. Built CI/CD frameworks for automated deployment, testing, and version control of data pipelines using Git, Jenkins, and Azure DevOps. Developed robust data quality and validation frameworks to ensure dataset integrity and reliability. Managed large, distributed clusters and fine-tuned Spark jobs for optimal performance and scalability. Designed real-time analytics dashboards with Power BI and Tableau for actionable business insights. Wrote complex SQL queries, stored procedures, triggers, and functions for data transformation and reporting. Processed geospatial and time-series datasets using specialized algorithms and frameworks. Created monitoring frameworks using AWS CloudWatch, Azure Monitor, and custom logging systems. Implemented data security and governance measures including RBAC, encryption, GDPR, and HIPAA compliance. Integrated hybrid data sources between on-premises and cloud environments. Mentored and guided junior data engineers, fostering collaborative learning. Collaborated with stakeholders to translate business requirements into scalable technical solutions. Operated effectively within Agile and Scrum frameworks (sprint planning, daily stand-ups, retrospectives). Delivered high-quality solutions on time while balancing technical and business priorities. Researched and adopted emerging data engineering tools and technologies. Transformed raw data into meaningful insights that drive business decisions. Change my professional summary for a business analyst position Senior engineering professional with deep expertise in data architecture, pipeline development, and big data technologies. Proven track record in optimizing data workflows, enhancing system efficiency, and driving business intelligence initiatives. Strong collaborator, adaptable to evolving project demands, with focus on delivering impactful results through teamwork and innovation. Skilled in SQL, Python, Spark, and cloud platforms, with strategic approach to data management and problem-solving.

Senior Data Engineer with over 10 years of experience in data engineering and software development. Expert in optimizing data pipelines, reducing costs, and enhancing data quality. Achievements include reducing daily data acquisition costs by 90%, achieving a 75% cost reduction in Glue job environment, and architecting solutions that led to a significant growth in online sales. Skilled in AWS, Azure, ETL tools, data modeling, and distributed systems. Seeking a Data Engineer position to leverage expertise in building efficient data processing systems and driving analytical insights to support the company's mission of revolutionizing entertainment through data-driven decision-making.

● Over 11+ years of experience in Data Engineering, Data Pipeline Design, Development and Implementation as a Sr. Data Engineer/Data Developer.
● Configure Zookeeper to coordinate and support Kafka, Spark, Spark Streaming, HBase and HDFS
● Setting up Azure infrastructure like storage accounts, integration runtime, service principal id, app registrations to enable scalable and optimized utilization of business user analytical requirements in Azure.
● Working on JSON scripts generation and writing UNIX shell scripting to call the SQOOP Import/Export
● Exploratory Data Analysis and Data Cleaning with Python.
● Have good knowledge on NoSQL databases like HBase, Cassandra and MongoDB.
● Have Extensive Experience in IT data analytics projects, Hands on experience in migrating on premise ETLs to Google Cloud Platform (GCP) using cloud native tools such as BIG query, Cloud Data Proc, Google Cloud Storage and Composer.
● Experienced implementation of a log producer in Scala that watches for application logs, transforms incremental log and sends them to a Kafka and Zookeeper based log collection platform.
● Experience in importing and exporting the data using Sqoop from HDFS to Relational Database systems and vice- versa and load into Hive tables, which are partitioned. Good knowledge in streaming applications using Apache Kafka.
● Experience in writing complex MapReduce jobs and Hive data modeling.
● Work experience with cloud infrastructure like Amazon Web Services (AWS) and AZURE.
● Expertise working with AWS cloud services like EMR, S3, Redshift, AWS Glue, EMR cloud watch, for big data development.
● Experience in fine-tuningMapReduce jobs for better scalability and performance and converting them to Spark.
● Experience in working with Spark RDD, Data Frames and Data Sets using different file formats like Json, Avro, Parquet and compression techniques.
● Worked extensively on enrichment/ETL in real time stream jobs using PySpark Streaming, Spark SQL and loads into HBase.
● Experienced in working with big data technologies like Spark Core, Spark SQL.
● Lambda functions for pre-processing data or post-processing Glue Job results.
● S3 events to trigger the Step Functions workflow when new data arrives in a specific S3 bucket.
● Implemented Step Functions to create new state machines and start the execution of Glue jobs.
● Orchestrated the workflow using AWS Step Functions to chain multiple AWS Services like Lambda, Glue in a defined sequence.
● Experience in working with Apache Flink and Supporting the jobs consuming Json data from Apache Pulsar.
● Experience in working with Apache Flink Data Streams and loading the data to PSQL.
● Experience in GCP Dataproc, GCS, Cloud functions, BigQuery.
● Working in relational SQL and NoSQL databases, including Oracle, Hive, Sqoop and HBase
● Designed and executed Oozie workflows in a manner that allowed for scheduling Sqoop and Hive job actions to extract, transform and load data
● Migrate databases to cloud platform SQL Azure and as well the performance tuning.
● Experienced on Hadoop/Hive on AWS, using both EMR and non-EMR-Hadoop in EC2.
● Experience developing Kafka producers and Kafka Consumers for streaming millions of events per second on streaming data.
● Developed ETL/ELT pipelines using Apache Spark on Azure Databricks, including data cleaning, data enrichment, and data aggregation using Spark SQL and Spark DataFrames.
● Written Map Reduce code to process and parsing the data from various sources and storing parsed data into HBase and Hive using HBase-Hive Integration
● Implemented sentiment analysis and text analytics on Twitter social media feeds and market news using Scala and Python.
● Provided production support for ETL and reporting systems, investigating and resolving issues, and maintaining system stability and availability. `
● Very keen in knowing newer techno stack that Google Cloud platform (GCP) adds.
● Hands on experience in installing, configuring Cloudera Apache Hadoop ecosystem components like Flume, HBase, Zookeeper, Oozie, Hive, Sqoop and Pig.
● Installed Hadoop, Map Reduce, HDFS, and AWS and developed multiple Map Reduce jobs in PIG and Hive for data cleaning pre-processing. and
● Worked with real-time data processing and streaming techniques using Spark streaming and Kafka
● Pipeline development skills with Apache Airflow, Kafka, and NiFi.
● Extensively using open-source languages Python, Scala and Java.
● Migrated projects from Cloudera Hadoop Hive storage to Azure Data Lake Store to satisfy Confidential transformation strategy
● Doing data synchronization between EC2 and S3, Hive stand-up, and AWS profiling.
● Using Spark Data frame API in Scala for analyzing data.

Cathy Rudolph

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Senior Data Engineer

Senior Implementation Specialist

Senior Implementation Specialist

Education

Master of Arts - Microbiology

Bachelor of Arts - Biology

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Senior Data Engineer

Senior Implementation Specialist

Senior Implementation Specialist

Master of Arts - Microbiology

Bachelor of Arts - Biology

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