Senior Data Analytics Engineer, AWS Pipelines

Ryan Berti

Senior Data Analytics Engineer

****.*.*******@*****.*** +1-650-***-**** Los Angeles, CA 90027 PROFESSIONAL SUMMARY

Senior Data Analytics Engineer with more than 14 years of experience designing scalable data platforms, ETL pipelines, and cloud-native analytics solutions across streaming, advertising, and enterprise data environments. Extensive expertise in Python, Pandas, NumPy, SQLAlchemy, SQL, AWS, APIs, automation, curated datasets, data quality validation, and historical data management aligned with audit-ready analytics. Proven success building reliable data pipelines, strengthening governance, maintaining secure cloud platforms, and mentoring engineering and analytics teams. Strong background delivering high-quality analytical datasets, operational excellence, and AI-enabled data solutions supporting business risk assessment and data-driven decision making. TECHNICAL SKILLS

Programming: Python, SQL, Pandas, NumPy, SQLAlchemy Cloud: AWS, Amazon S3, Amazon EC2, AWS Lambda, AWS Glue, Amazon Athena, AWS IAM, AWS CloudShell, Terraform

Data Engineering: ETL, Data Pipelines, API Integration, Data Quality, Data Validation, Data Transformation, Data Modeling, Historical Data Management, Metadata Management Analytics: Audit Analytics, Risk Assessment, Control Testing, Data Governance, Audit Logging, Evidence Chain, Reporting, AI, Automation

Platforms: Git, Linux, JSON, REST APIs, CI/CD, Monitoring, Documentation WORK EXPERIENCE

Netflix Los Angeles Metropolitan Area

Senior Data Engineer Jan 2021 - Present

• Designed enterprise data pipelines using Python, Pandas, SQLAlchemy, SQL, AWS Glue, Lambda, S3, and Athena to curate trusted analytical datasets supporting global streaming operations with reliable historical traceability.

• Developed automated ingestion frameworks connecting REST APIs, relational databases, and event sources while enforcing comprehensive validation rules that significantly reduced downstream data inconsistencies by approximately 38%.

• Implemented scalable ETL workflows that generated timestamped datasets for regulatory reporting, analytical reproducibility, and long-term governance across high-volume distributed data platforms.

• Built reusable Python libraries that standardized schema validation, metadata capture, provenance tracking, and exception handling, accelerating onboarding of new analytical data sources across engineering teams.

• Maintained cloud infrastructure using Terraform, IAM, EC2, S3, and Lambda while improving operational resilience through automated backup, patching, and infrastructure consistency validation.

• Created automated data quality monitoring with statistical validation, anomaly detection, lineage verification, and alerting to ensure trustworthy datasets supporting executive business decisions.

• Partnered with analytics, governance, and platform engineering teams to operationalize secure self-service datasets while maintaining strong access controls and comprehensive audit logging.

• Optimized SQL processing strategies, partitioning techniques, and data transformations to reduce large-scale analytical processing time by nearly 41% without sacrificing data integrity.

• Documented ingestion standards, field definitions, transformation logic, and operational procedures to eliminate knowledge silos and improve long-term platform maintainability.

• Mentored engineers on cloud-native data engineering, ETL optimization, testing practices, automation techniques, and production support for highly available analytics environments.

• Integrated AI-assisted validation workflows that identified schema drift, unusual distributions, and pipeline anomalies before production publication, improving confidence in analytical outputs.

• Delivered solution combining immutable audit logs with incremental processing despite rapidly evolving source schemas across billions of records, improving governed dataset availability with measurable operational stability. Quibi Los Angeles, CA

Senior Data Engineer Nov 2019 - Nov 2020

• Engineered cloud-based ETL pipelines integrating API data, relational databases, and streaming metadata into curated analytical repositories using Python, SQL, and AWS services.

• Developed automated validation routines with Pandas and NumPy that verified completeness, consistency, and business rules before publishing production-ready datasets for analytics consumers.

• Created metadata-driven transformation frameworks supporting reproducible historical reporting while preserving dataset lineage and detailed processing documentation.

• Implemented infrastructure automation using Terraform together with AWS services, simplifying deployment consistency and reducing manual operational effort by approximately 29%.

• Collaborated with analytics stakeholders to define standardized schemas, business definitions, and reusable datasets supporting operational reporting and executive dashboards.

• Strengthened monitoring, logging, backup automation, and operational documentation to improve production reliability while enabling efficient knowledge transfer across engineering teams. The Walt Disney Company Glendale

Data Platform Engineer Aug 2019 - Nov 2019

• Built Python and SQL data preparation workflows transforming operational source systems into governed analytical datasets supporting enterprise reporting initiatives across media operations.

• Integrated diverse APIs and database sources into centralized cloud storage while implementing rigorous validation logic that improved trusted analytical data availability.

• Established documentation standards covering field definitions, transformation logic, and lineage information supporting transparent governance and long-term maintainability.

• Collaborated with cross-functional engineering and analytics teams to automate recurring data preparation tasks while strengthening quality assurance throughout production workflows. OpenX Pasadena, CA

Data Engineer May 2016 - Aug 2019

• Designed distributed ETL pipelines processing advertising platform datasets using Python, SQL, APIs, and cloud technologies to support large-scale analytical reporting.

• Developed automated data quality frameworks validating transactional accuracy, schema consistency, and ingestion completeness before downstream analytical consumption.

• Created reusable SQLAlchemy integration components simplifying connectivity across heterogeneous databases and improving engineering productivity for new pipeline development.

• Implemented cloud storage optimization, partitioning strategies, and transformation workflows reducing analytical query latency by approximately 34% across high-volume datasets.

• Built comprehensive metadata repositories documenting data provenance, field definitions, operational assumptions, and transformation history for governed analytics.

• Collaborated with platform engineers to maintain secure infrastructure, automate deployments, and improve production observability across distributed processing environments.

• Mentored engineers on ETL architecture, Python optimization, SQL tuning, testing methodologies, and operational excellence supporting scalable analytical platforms.

• Delivered robust ingestion frameworks preserving historical snapshots, auditability, and consistent lineage for rapidly changing advertising datasets supporting reliable business intelligence. Teradata San Diego, CA

Data Engineer May 2012 - May 2016

• Developed enterprise data integration solutions using Python, SQL, and ETL methodologies supporting large-scale analytical platforms across complex customer environments.

• Implemented automated extraction, cleansing, transformation, and validation processes ensuring trusted datasets for reporting, forecasting, and operational decision support.

• Designed optimized SQL workflows, indexing strategies, and scalable data models improving analytical performance while maintaining strong governance standards.

• Integrated multiple enterprise applications through APIs and database connectivity while standardizing ingestion processes for consistent downstream consumption.

• Created detailed operational documentation describing data lineage, transformation logic, validation procedures, and support processes for long-term platform sustainability.

• Collaborated with cross-functional engineering teams to automate deployment, testing, monitoring, and production support for mission-critical analytical workloads.

• Delivered solution balancing transformation flexibility against strict performance constraints across multi-terabyte enterprise datasets, improving processing efficiency with measurable operational gains.

• Delivered solution introducing reusable validation frameworks despite heterogeneous source systems at enterprise scale, reducing production data defects while improving engineering maintainability. EDUCATION

University of Southern California 2012

Master of Science in Computer Science

University of Southern California 2010

Bachelor of Science in Computer Science

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