Big Data Engineer Resume Example

Professional Summary

Big data engineer with 6 years designing distributed data processing systems at petabyte scale. Expert in Spark, Hadoop, and cloud-native big data platforms with experience building batch and streaming architectures for real-time analytics and machine learning pipelines.

Key Skills

What to Include on a Big Data Engineer Resume

• A concise summary that states your big data engineer experience level, strongest domain, and the business problems you solve.
• A skills section that mirrors the job description language for Apache Spark, Hadoop (HDFS, YARN), Kafka, Hive/Presto.
• Experience bullets that connect big data engineer, Spark engineer, distributed computing to measurable outcomes such as cost savings, faster delivery, better quality, or improved customer results.
• Tools, platforms, certifications, and methods that are current for data & analytics roles.
• Recent projects that show ownership, cross-functional work, and a clear result instead of generic responsibilities.

Sample Experience Bullets

• Built a petabyte-scale data lake on S3 with Delta Lake providing ACID transactions, schema enforcement, and time travel capabilities that ingests 20TB+ daily from 100+ sources including IoT sensors, application logs, and transactional databases. The lake serves as the foundation for all analytics workloads
• Designed a real-time streaming pipeline using Kafka and Flink that processes 1M+ events per second for fraud detection with sub-second end-to-end latency from event ingestion to alert generation. The pipeline has maintained 99.99% uptime over 12 months of continuous operation
• Tuned Spark jobs that were running for 8+ hours by fixing partition skew through salted keys, replacing expensive shuffles with broadcast joins, and enabling adaptive query execution. Total cluster runtime dropped by 55% and monthly compute costs decreased by $25K
• Migrated 500+ Hive batch jobs to Spark on Databricks with a systematic approach covering job conversion, performance validation, and parallel running periods for each migration batch. Processing speed improved 10x on average and compute costs dropped 35%
• Implemented a lakehouse architecture using Delta Lake that supports both batch analytics and real-time streaming queries on the same data store, eliminating the need for a separate data warehouse. The consolidation reduced infrastructure complexity and saved about $200K annually
• Managed the Databricks workspace for 30+ data engineers and scientists, configuring cluster policies, access controls, workspace organization, and cost monitoring. Set up auto-scaling policies that keep compute costs predictable while handling variable workload demands
• Worked with the ML team to build feature engineering pipelines in Spark that transform raw event data into training-ready feature tables, processing 50+ features across 10M+ records daily. The pipelines include data validation checks and feature drift monitoring
• Wrote data quality checks that run at each stage of the pipeline including schema validation, null rate monitoring, value distribution comparisons, and cross-table referential integrity checks. The checks catch data issues before bad data reaches downstream analytical consumers
• Maintained Terraform configurations for all data infrastructure including EMR clusters with auto-scaling policies, S3 buckets with lifecycle rules, Glue crawlers and jobs, and IAM roles with least-privilege permissions. Infrastructure changes go through code review like application code
• Built a data compaction and optimization service that runs nightly to consolidate small files in the data lake into optimally-sized Parquet files with Z-order clustering on commonly filtered columns. Query performance on the compacted data improved by 3-5x for typical analytics queries
• Designed a multi-tenant data isolation framework that separates customer data at the storage layer using partition-based access controls, allowing the analytics team to run cross-tenant analyses while maintaining strict data boundaries required by enterprise client contracts

ATS Keywords for Big Data Engineer Resumes

Use these terms naturally where they match your experience and the job description.

Recommended Certifications

• Databricks Certified Data Engineer Professional
• Cloudera Certified Professional

What Does a Big Data Engineer Do?

• Design, develop, and maintain software solutions using Apache Spark, Hadoop (HDFS, YARN), Kafka and related technologies
• Collaborate with cross-functional teams including product managers, designers, and QA engineers to deliver features on schedule
• Write clean, well-tested code following industry best practices for big data engineer and Spark engineer
• Participate in code reviews, technical discussions, and architecture decisions to improve system quality and team knowledge
• Troubleshoot production issues, optimize performance, and ensure system reliability across all environments

Resume Tips for Big Data Engineers