Junior Data Engineer Resume Example

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Junior Data Engineer Resume Sample

Kevin Nguyen
kevin@nguyen.dev
•
(408) 555-0380
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linkedin.com/in/kevin-nguyen-data
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github.com/kevinnguyen
Junior Data Engineer
Junior Data Engineer with 2 years building data pipelines and ETL processes. Proficient in Python, SQL, Airflow, and AWS. Built 8 data pipelines processing 500K+ records daily, improved data quality by 35%, and reduced pipeline runtime by 25%. Passionate about data architecture, automation, and scalable data systems.
WORK EXPERIENCE
Junior Data Engineer
Oct 2023 – Present
Analytics SaaS Startup
  • Data Pipeline Development: Built 8 production data pipelines using Python and Airflow processing 500K+ records daily from APIs, databases, and files into Snowflake data warehouse
  • Data Quality & Optimization: Improved data quality by 35% through implementing validation checks, automated testing, and monitoring, reduced pipeline runtime by 25% through optimization
  • ETL & Transformation: Developed ETL processes and dbt models transforming raw data into analytics-ready tables, documented data lineage and created data quality dashboards
Data Engineering Intern
Jun 2022 – Sep 2023
E-Commerce Platform
  • Pipeline Implementation: Implemented 5 batch ETL pipelines in Python extracting data from PostgreSQL, APIs, and S3, loading into data warehouse for analytics
  • Data Modeling: Designed dimensional data models and fact tables for product analytics, implemented slowly changing dimensions (SCD Type 2)
  • Automation & Monitoring: Automated data pipeline scheduling with Airflow, set up alerts for pipeline failures, documented data workflows and dependencies
SKILLS & COMPETENCIES
Python (pandas, PySpark) | SQL (PostgreSQL, MySQL) | Apache Airflow | AWS (S3, Glue, Redshift) | Snowflake | dbt (Data Build Tool) | Apache Spark Basics | Data Modeling | ETL/ELT Processes | Git & GitHub | Data Quality & Testing | Agile/Scrum
CERTIFICATIONS
AWS Certified Data Analytics Specialty
Jul 2024
Amazon Web Services
EDUCATION
Bachelor of Science in Computer Science
2019-2023
University of Michigan
Ann Arbor, Michigan
  • Data Science
  • Database Systems

Tools to build your Junior Data Engineer resume

Copy and adapt these proven examples to create a resume that stands out.

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Resume Headlines

Use these attention-grabbing headlines to make a strong first impression.

Junior Data Engineer | Python, SQL, Airflow | Processing 500K+ Daily Records
Data Engineer | ETL & Data Pipelines | 35% Data Quality Improvement
Junior Data Engineer | AWS, Snowflake, dbt | Building Scalable Pipelines
Data Engineer | Modern Data Stack | Python, Airflow, dbt, AWS
Junior Data Engineer | Data Quality Focus | 8 Production Pipelines
Data Engineer | ETL Development | Batch & Real-Time Processing

💡 Tip: Choose a headline that reflects your unique value proposition and matches the job requirements.

⚡

Power Bullet Points

Adapt these achievement-focused bullets to showcase your impact.

Data Pipeline Development

• Built 8 production data pipelines using Python and Apache Airflow processing 500K+ records daily from REST APIs, databases, and S3 into Snowflake data warehouse
• Developed end-to-end ETL workflows extracting data from 12 sources (PostgreSQL, MySQL, APIs, files), transforming with dbt, and loading into dimensional models
• Implemented incremental data loading strategies reducing processing time by 25% and costs by 30% through optimized queries and partitioning
• Created real-time streaming pipeline using AWS Kinesis and Lambda processing 50K+ events/hour with sub-second latency for analytics dashboards

Data Quality & Testing

• Improved data quality by 35% through implementing dbt tests, Great Expectations validation, and automated data quality checks catching 100+ data issues pre-production
• Built data quality monitoring dashboards tracking freshness, completeness, and accuracy metrics alerting teams to 95% of issues before business impact
• Developed data validation framework testing schema compliance, null checks, and referential integrity across 20+ data models reducing downstream errors by 40%
• Documented data lineage and dependencies using dbt docs and data catalogs enabling 30+ analysts to understand data sources and transformations

ETL & Data Transformation

• Developed 25+ dbt models transforming raw data into analytics-ready fact and dimension tables serving 30+ analysts and data scientists
• Designed dimensional data models (star schema) for product analytics implementing slowly changing dimensions (SCD Type 2) tracking historical changes
• Built aggregation tables and materialized views reducing query times from 45s to 3s improving dashboard performance for 50+ users
• Implemented data deduplication and cleansing logic improving data accuracy by 35% and enabling reliable business reporting

Orchestration & Automation

• Orchestrated 15+ data workflows using Apache Airflow with DAGs, sensors, and dependencies automating previously manual 8-hour processes
• Set up monitoring and alerting with Airflow and PagerDuty reducing mean time to detection (MTTD) from 4 hours to 15 minutes for pipeline failures
• Automated data pipeline scheduling reducing manual intervention by 80% and improving data freshness from 6 hours to 1 hour
• Implemented retry logic, error handling, and data backfill processes improving pipeline reliability from 92% to 99.2%

💡 Tip: Replace generic terms with specific metrics, technologies, and outcomes from your experience.

📝

Resume Writing Tips for Junior Data Engineers

1

Emphasize ETL and Pipeline Experience

Junior data engineers are judged on execution. Highlight: pipelines built (8), records processed (500K daily), data sources integrated (APIs, databases, files). Show you ship data pipelines, not just write SQL—you build production systems.

2

Quantify Data Quality and Performance

Data engineering impact is measurable. Include: data quality improvements (35% better), pipeline runtime (25% faster), cost reductions (30% savings), users served (30+ analysts). Show you care about quality and efficiency, not just "getting data there."

3

Show Modern Data Stack Proficiency

List 10-12 skills covering Python (pandas, PySpark), SQL, orchestration (Airflow), cloud (AWS, Snowflake), transformation (dbt), and testing. Show you understand the modern data stack—not just legacy ETL tools.

4

Highlight Data Quality and Testing

Data quality differentiates junior engineers. Include: validation checks, automated testing (dbt tests, Great Expectations), monitoring, documentation. Show maturity—you ensure data is right, not just moved.

5

Balance Batch and Real-Time Processing

Show breadth: batch ETL for fundamentals (Airflow, dbt), streaming for growth (Kinesis, Kafka basics). Junior engineers need batch mastery but should show awareness of real-time—it demonstrates forward-thinking.

🎯

Essential Skills & Keywords

Include these skills to optimize your resume for ATS systems and recruiter searches.

Programming & Scripting

Python pandas PySpark SQL (Advanced) Bash/Shell Scripting

Data Warehousing

Snowflake AWS Redshift BigQuery Data Modeling Star Schema SCD (Slowly Changing Dimensions)

Orchestration & Workflow

Apache Airflow DAG Development Workflow Automation Scheduling Monitoring & Alerting

Cloud Platforms

AWS (S3, Glue, Lambda, Redshift) AWS Kinesis Cloud Storage Serverless

Data Transformation

dbt (Data Build Tool) ETL/ELT Processes Data Cleaning Data Validation Data Lineage

Data Quality & Testing

Great Expectations dbt Tests Data Validation Quality Monitoring Documentation

Big Data & Streaming

Apache Spark Basics Streaming (Kinesis basics) Batch Processing Partitioning Incremental Loading

Best Practices

Version Control (Git) CI/CD Basics Agile/Scrum Documentation Code Reviews

💡 Tip: Naturally integrate 8-12 of these keywords throughout your resume, especially in your summary and experience sections.

Why this resume works

Role-Specific Strengths

  • ETL and pipeline development: Built 8 data pipelines, processed 500K records daily—demonstrates hands-on data engineering fundamentals
  • Data quality focus: 35% data quality improvement—shows understanding of data validation, testing, monitoring
  • Performance optimization mindset: 25% pipeline runtime reduction—demonstrates efficiency thinking beyond just getting pipelines working
  • Modern data stack proficiency: Python, SQL, Airflow, AWS, dbt—shows relevant tooling for entry-level data engineers

✓ ATS-Friendly Elements

  • Entry-level keywords: "data pipeline," "ETL," "Python," "SQL," "Airflow," "AWS," "data quality"
  • Action verbs: Built, Developed, Implemented, Optimized, Automated
  • Technologies: Python, SQL, Airflow, AWS, dbt, Snowflake, Spark
  • Practices: data modeling, data quality, pipeline orchestration, version control
  • Quantified contributions: pipelines built, records processed, quality improvements

✓ Human-Readable Design

  • Summary emphasizes data engineering execution: built pipelines, improved quality, optimized performance
  • Metrics scaled appropriately: 8 pipelines, 500K records, 35% quality gains, 25% speed improvements
  • Experience shows progression from intern to junior data engineer
  • Skills balance SQL, Python, orchestration (Airflow), cloud (AWS), and transformation (dbt)
  • Recent degree or bootcamp with data focus signals entry level

💡 Key Takeaways

  • Junior data engineers should emphasize ETL development, data quality, and pipeline orchestration
  • Quantify your work: pipelines built, records processed, data quality metrics, runtime improvements
  • Show modern data stack: Python, SQL, Airflow, cloud platforms, dbt, Spark
  • Highlight data quality: validation, testing, monitoring, documentation
  • Balance batch and streaming: batch ETL for fundamentals, streaming for growth

📈 Career Progression in Data Engineering

See how Data Engineering roles evolve from pipeline development to platform architecture.

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