Sara Wood

Lead Data Engineer with 8+ years of experience in Data Engineering, Distributed Systems, and Cloud-Native Architectures across FinTech, E-commerce, Banking, and SaaS domains. Expertise in building Scalable Data Lakes, Lakehouse Architectures, and ETL/ELT Pipelines for Enterprise Analytics and Machine Learning use cases. Strong background in Apache Spark, Real-Time Streaming, and Data Pipeline Optimization.

Proven experience with Cloud Platforms (AWS, GCP, Azure), Big Data Technologies, and Data Platform Engineering, delivering high-performance systems processing billions of events with improved data reliability, scalability, and performance.

Lead Data Engineer - Databricks

(2024-07)

• Architected data platform processing ~10M events/day from multiple upstream systems
• Built real-time streaming pipeline using Kafka → Spark Structured Streaming → Snowflake for near real-time analytics
• Designed and maintained Databricks Lakehouse (Bronze, Silver, Gold layers) for batch and streaming data processing
• Led migration from Hadoop to AWS (S3, Glue, EMR) with Snowflake and Databricks integration
• Implemented CDC (Change Data Capture)-based ingestion pipelines for critical datasets
• Built reusable ETL framework using Airflow and Spark jobs, reducing pipeline duplication across teams
• Improved Spark performance tuning (partitioning, shuffle optimization), reducing job runtime from ~2 hours to ~45 minutes
• Implemented data quality checks (row counts, null checks) for critical data pipelines
• Supported production incident debugging, resolving data latency and pipeline failures
• Mentored junior data engineers and participated in design reviews and code reviews
• Built end-to-end supply chain analytics data models and KPI matrices, improving inventory visibility, demand forecasting, and order fulfillment metrics by 40%+ across distributed systems
• Integrated ERP systems and logistics data sources (SAP / third-party APIs) into a centralized lakehouse architecture, improving data consistency, reporting accuracy, and business metrics reliability by 30–35%
• Implemented data security and governance controls (RBAC, encryption, PII masking) across data pipelines, improving data compliance and reducing security risks by 35%+
• Enhanced ETL pipeline observability and monitoring using Airflow/Spark logs and alerting, reducing production incident resolution time by 25–30%

Senior Data Engineer - Wayfair

(2021-06 - 2024-06)

• Built ETL pipelines using Airflow, Spark, and SQL-based transformations (dbt-style models), processing 500M+ records daily and reducing pipeline runtime by 30%
• Developed and maintained Snowflake data warehouse supporting 50+ BI dashboards and enterprise reporting for 200k+ users
• Integrated multiple data sources (events, orders, user activity, payments), improving reporting accuracy by 25%, into unified analytics datasets
• Designed and maintained Customer 360 dataset, enabling analytics and product teams to increase campaign targeting efficiency by 20%
• Built feature engineering pipelines for recommendation systems and customer segmentation, improving ML model CTR by 15–20%
• Implemented data validation checks that detected and resolved >99% of broken/incomplete daily data loads automatically
• Optimized SQL queries and Spark transformations, reducing reporting workflow runtimes by 40%

Data Engineer - Mindtree

(2019-04 - 2021-05)

• Built batch data pipelines using Hadoop ecosystem (Hive, HDFS, Sqoop), handling 100M+ transactions daily
• Developed ETL workflows for daily transaction processing and financial reporting, reducing processing errors by 30%
• Designed Star Schema data models, improving query performance by 25% for risk and financial analytics
• Supported regulatory reporting jobs (daily, weekly, monthly loads), achieving 100% on-time compliance delivery
• Worked on large transactional datasets for fraud and anomaly detection, increasing detection accuracy by 10–15%
• Fixed data quality issues in production pipelines, reducing scheduled job failures by 35%

Junior Data Engineer - Cognizant

(2018-02 - 2019-03)

• Built SQL-based ETL scripts for data extraction and transformation of client datasets
• Developed Python automation scripts for data loads and file processing workflows
• Scheduled and maintained batch jobs using cron-based scheduling workflows
• Performed data cleaning and transformation for reporting datasets and analytics use cases
• Assisted in data migration between relational databases for enterprise systems

Bachelor's Degree - Computer Science