Zara Blackwood
Big Data Executive
| Phone: | (415) 555-0198 |
| Address: | San Francisco, CA |
| Website: | https://linkedin.com/in/zarablackwood |
| Email: | [email protected] |
- Big Data Engineer with 2+ years designing scalable data pipelines using Apache Spark 3.2+, Hadoop 3.3, and AWS EMR
- Reduced data processing time by 65% through optimization of ETL workflows handling 15TB+ daily transaction data
- Built real-time streaming applications using Kafka and Flink processing 200K+ events per second with 99.9% uptime
- Experienced in distributed computing frameworks, cloud-native architectures, and performance optimization for petabyte-scale datasets
Work Experience
StreamTech Solutions
Junior Data Engineer
June 2022 - Present
- Architected distributed data processing pipeline using Apache Spark 3.2+ on Kubernetes, reducing batch processing time from 6 hours to 2.1 hours for 800GB daily customer behavior data
- Implemented real-time anomaly detection system using Kafka Streams and Cassandra, identifying fraudulent transactions with 96% accuracy and reducing false positives by 30%
- Optimized Hive queries and Spark configurations resulting in 75% reduction in cluster resource usage and $22K monthly AWS cost savings
- Built automated data quality framework using Great Expectations, reducing data incidents by 85% and improving downstream analytics reliability
- Mentored 2 junior engineers on Spark optimization techniques, distributed computing best practices, and debugging failed MapReduce jobs
DataFlow Innovations
Data Analytics Intern
January 2022 - May 2022
- Developed ETL pipelines using PySpark processing 50GB+ of IoT sensor data daily, improving data ingestion speed by 40%
- Created real-time dashboard using Kafka Connect and Elasticsearch, enabling stakeholders to monitor 10K+ connected devices
- Implemented data partitioning strategies in HDFS reducing query response times from 45 seconds to 8 seconds for analytical workloads
- Collaborated with senior engineers to migrate legacy batch processes to streaming architecture using Apache Flink
Stanford University
Research Assistant - Distributed Computing Lab
September 2021 - December 2021
- Built distributed machine learning pipeline processing 2TB+ of social media data using Spark MLlib and custom partitioning strategies
- Optimized memory management for large-scale graph processing, achieving 3x performance improvement on 100M+ node networks
- Published research on stream processing optimization techniques, contributing to Apache Beam Python SDK performance improvements
Technical Skills
Big Data Processing: Apache Spark 3.2+ (PySpark, Spark SQL, Spark Streaming, MLlib), Hadoop Ecosystem (HDFS, YARN, MapReduce), Apache Kafka, Apache Flink, Apache Beam
Programming Languages: Python (pandas, NumPy, scikit-learn, PySpark), Scala, Java, SQL, R
Data Storage & Databases: HDFS, Amazon S3, Azure Data Lake Storage, Apache Cassandra, MongoDB, HBase, PostgreSQL, Delta Lake, Apache Iceberg
Cloud Platforms: AWS (EMR 6.5+, Glue, Athena, Kinesis, S3), Azure (Databricks, Synapse Analytics, Event Hubs), GCP (Dataflow, BigQuery, Pub/Sub)
Data Processing & Analytics: Apache Hive, Presto, Apache Airflow, Databricks, Jupyter Notebooks, Apache Zeppelin
DevOps & Monitoring: Docker, Kubernetes, Apache Kafka Manager, Grafana, Prometheus, ELK Stack (Elasticsearch, Logstash, Kibana)
Technical Leadership: Data architecture design, technical documentation, cross-functional collaboration with data science and business intelligence teams, performance optimization, scalability planning for 10x data growth scenarios
Education
Stanford University
Master of Science in Computer Science - Data Systems Track
May 2022
Relevant Coursework: Distributed Computing, Large-Scale Data Mining, NoSQL Database Systems, Stream Processing, Machine Learning at Scale, Statistical Learning Theory
Thesis: "Optimizing Apache Spark Performance for Real-Time Graph Analytics on Petabyte-Scale Social Networks"
Key Projects:
- Built distributed recommendation engine processing 5TB+ of user interaction data using collaborative filtering on Spark, achieving 94% accuracy
- Developed real-time fraud detection pipeline using Kafka and Flink, processing 150K transactions/second with sub-100ms latency
University of California, Berkeley
Bachelor of Science in Applied Mathematics | Data Science Concentration
May 2020
Magna Cum Laude, GPA: 3.8/4.0
Key Projects:
- Built distributed image classification system processing 8TB dataset using PySpark and TensorFlow, achieving 97% accuracy on 10M+ images
- Developed streaming analytics platform using Kafka Streams analyzing Twitter data at 50K tweets/second for sentiment analysis
Professional Certifications
AWS Certified Big Data - Specialty
Amazon Web Services
2023
Databricks Certified Associate Developer for Apache Spark 3.0
Databricks
2023
Google Cloud Professional Data Engineer
Google Cloud
2022
Cloudera Certified Professional: Data Engineer
Cloudera
2022
Awards & Recognition
2nd Place, Netflix Big Data Challenge 2023
Netflix
2023
- Developed distributed recommendation algorithm processing 200M+ user interactions using ensemble methods on Spark
- Improved prediction accuracy by 28% over baseline models while reducing training time by 45%
Winner, Stanford Data Mining Competition 2022
Stanford University
2022
- Analyzed 3TB+ of IoT sensor data to predict equipment failures using time series analysis and distributed computing
- Solution implemented by campus facilities management, preventing $75K in equipment downtime annually
Outstanding Graduate Student Award - Computer Science Department
Stanford University
2022
- Recognized for thesis work on Spark optimization and contributions to distributed systems research
Publications & Technical Writing
Optimizing Spark Performance for Time Series Analysis at Scale
DataEngineering.io
Featured article with 8,000+ views, includes working code examples and performance benchmarks
Real-time Stream Processing: Kafka vs. Pulsar Performance Comparison
Medium - Towards Data Science
Implemented comprehensive benchmarks processing 1M+ events/second, 12K+ claps
Open Source Contribution: Apache Beam Python SDK
Apache Foundation
Merged pull request improving windowing function performance by 20% for streaming workloads
Featured GitHub Projects
spark-optimization-toolkit
https://github.com/zarablackwood
Custom Spark transformations and utilities reducing shuffle operations by 45% and memory usage by 30%
real-time-anomaly-detector
https://github.com/zarablackwood
Kafka Streams application processing 100K+ events/second with machine learning-based anomaly detection
data-quality-framework
https://github.com/zarablackwood
Automated testing and validation suite for petabyte-scale data pipelines with configurable quality rules
Technical Presentations
Building a 12TB/day Analytics Pipeline on a Startup Budget
PyCon 2023 Lightning Talk
2023
- Presented cost-optimization strategies reducing cloud spend by 65% using spot instances and auto-scaling
- Slides downloaded 750+ times, implementation adopted by 5+ startups