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Click here or scroll down to respond to this candidateCandidate's Name
Principal Data Engineer | Lead Data Engineer
Phone: PHONE NUMBER AVAILABLE
Email: EMAIL AVAILABLE
Address: Hoboken New Jersey, United States, Street Address
Professional Summary:
I bring over a decade of experience in data engineering, machine learning, specializing in crafting and
optimizing data pipelines, managing cloud-native architectures, and deploying advanced analytics
solutions. At Company 1, I focused on building reliable ETL pipelines and maintaining data warehousing
systems. My role as Lead Data Engineer at Company 2 involved leading a team to enhance data processing
efficiency and implementing best practices in DataOps. Now, as a Principal Data Engineer, I design and
implement scalable data lakes, real-time processing systems, and sophisticated MLOps pipelines, providing
innovative solutions that drive business growth and decision-making.
Skills:
Data Engineering
ETL (Extract, Transform, Load) processes
Data pipeline development and optimization
Data warehousing and storage solutions (e.g., AWS Redshift, Google BigQuery)
Database management (SQL, NoSQL, relational databases)
Data modeling and schema design
Distributed data systems (e.g., Hadoop, Spark)
Cloud services for data (AWS, Azure, GCP)
Machine Learning
Model development, training, and deployment
Feature engineering and data preprocessing
Model evaluation and optimization
MLOps (CI/CD for machine learning)
Familiarity with frameworks such as TensorFlow, PyTorch, or Scikit-learn
Implementing AI/ML pipelines in production
Data Science
Statistical analysis and hypothesis testing
Data visualization (e.g., Matplotlib, Seaborn, Power BI)
Predictive analytics and forecasting
Business intelligence and decision-making insights
Natural Language Processing (NLP)
Big Data Technologies
Apache Hadoop ecosystem (e.g., HDFS, Hive, Pig)
Real-time data processing (e.g., Apache Storm, Apache Flink)
Stream processing technologies (e.g., Apache Kafka Streams, AWS Kinesis)
Data Governance & Compliance
Data security and privacy (e.g., GDPR, CCPA compliance)
Data quality management
Metadata management
Data lineage and auditing
Role-based access control (RBAC) and encryption strategies
Cloud-Native Data Solutions
Serverless data processing (e.g., AWS Lambda, Google Cloud Functions)
Data lake architectures (e.g., AWS S3, Azure Data Lake)
Managed cloud databases (e.g., Amazon RDS, Google Cloud SQL)
Advanced Analytics
Time series analysis and forecasting
Anomaly detection
Clustering and classification algorithms
Deep learning (e.g., convolutional neural networks, recurrent neural networks)
Reinforcement learning and optimization algorithms
DataOps
Automated testing for data pipelines
Version control for data assets (e.g., DVC)
Data cataloging (e.g., AWS Glue, Alation)
Data monitoring and alerting systems
Automation and Scripting
Bash scripting for system automation
Automating cloud resource provisioning and monitoring
Task automation using Python and shell scripts
CI/CD for Data Pipelines
Implementing automated testing for ETL workflows
Integration of continuous delivery for data solutions
Automating pipeline deployments with Jenkins, CircleCI, or GitLab CI
Visualization & Dashboarding
Advanced dashboard creation (e.g., Power BI, Looker, Tableau)
Custom visualizations using D3.js, Plotly, or Dash
Interactive reporting with Jupyter Notebooks
Cross-Industry Knowledge
Applying AI/ML in manufacturing (smart factories, IoT analytics)
Predictive maintenance models for industrial equipment
Optimization of supply chain and inventory management through data insights
Work Experience:
Principal Data Engineer
Pager Duty (Contractor) June 2022-Current
Architected cloud-native data solutions, implementing serverless data processing pipelines for
high-volume, low-latency data applications.
Designed and deployed real-time stream processing solutions, enabling real-time data analytics
and decision-making.
Led end-to-end automation, streamlining the model lifecycle from development to production for
advanced models.
Created and managed data lakes for scalable storage of structured and unstructured data.
Implemented strategies to ensure data security and compliance with industry standards.
Set up automated testing, deployment, and monitoring of complex workflows.
Optimized infrastructure and architecture to reduce operational costs while enhancing
performance and scalability.
Developed custom automated testing frameworks, ensuring robust and reliable data processing.
Consulted with multiple clients across industries to design bespoke data solutions, enhancing
their data-driven decision-making capabilities.
Lead Data Engineer
Udacity July 2018-June 2022
Led a team of data engineers in the design and optimization of scalable data pipelines that
processed millions of data points daily.
Architected distributed data systems to enable real-time data processing for analytics applications.
Implemented practices automating the deployment, testing, and monitoring of data pipelines to
reduce manual intervention and improve system reliability.
Collaborated with the data science team to streamline machine learning pipelines and deliver real-
time predictions in production environments.
Optimized SQL queries and performance-tuned databases to enhance query efficiency.
Introduced data governance practices, ensuring compliance with data privacy regulations and
maintaining high data quality standards.
Automated infrastructure provisioning and monitoring, streamlining setup processes and
improving scalability.
Data Engineer
Data Root Labs July 2014- June 2018
Designed and maintained ETL pipelines to transform raw data into structured data for business
analytics.
Managed relational and NoSQL databases, ensuring high availability and performance of data
storage solutions.
Implemented data models and optimized database schemas to improve query performance.
Built data pipelines using Python to automate routine data processing tasks.
Integrated cloud storage solutions like AWS S3 with on-premise data systems to ensure smooth
data migration and backup.
Developed and maintained data warehousing systems, providing structured and accessible data to
business intelligence teams.
Collaborated with stakeholders to identify data requirements and create scalable data engineering
solutions.
Monitored and troubleshot pipeline failures, ensuring data accuracy and system reliability.
Education:
Bachelor of Science
University of Houston Texas
2010 2014
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