Junior Analytics Engineer Resume Sample

Jordan Lee

jordan@lee.dev

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(415) 555-0380

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linkedin.com/in/jordan-lee-analytics

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github.com/jordanlee

Junior Analytics Engineer

Junior Analytics Engineer with 2 years building data pipelines and analytics infrastructure. Proficient in SQL, Python, dbt, and data modeling. Built 15+ data models serving 50+ stakeholders, improved data quality by 35%, and implemented data testing frameworks. Passionate about bridging data engineering and analytics, enabling self-service insights.

WORK EXPERIENCE

Junior Analytics Engineer

Aug 2023 – Present

E-Commerce Startup

Data Modeling & Transformation: Built 15+ dbt models transforming raw events, users, and orders into dimensional models serving 50+ stakeholders, enabling self-service analytics with <5-minute query times
Data Quality & Testing: Implemented dbt tests across all models catching 25+ data quality issues before production, improved data reliability by 35% and reduced data incidents from 8 to 2 per month
Pipeline Orchestration: Developed Airflow DAGs orchestrating dbt jobs and Python scripts, automated daily refreshes for 5 dashboards serving 100+ users with 99.2% uptime

Data Analyst (Analytics Engineering Focus)

Jun 2022 – Jul 2023

SaaS Scale-up

SQL & Data Analysis: Wrote 50+ complex SQL queries analyzing product usage, customer behavior, and revenue metrics, supporting 20+ stakeholders with ad-hoc analysis and reporting
dbt Model Development: Migrated 15 legacy SQL scripts to dbt models with tests and documentation, reducing technical debt and improving code maintainability by 40%
Dashboard Development: Built 8 Looker dashboards visualizing key product and business metrics, enabling executive team to track OKRs and make data-driven decisions

SKILLS & COMPETENCIES

CERTIFICATIONS

dbt Analytics Engineering Certification

Mar 2024

dbt Labs

EDUCATION

Bachelor of Science in Data Science

2018-2022

University of California, San Diego

San Diego, California

Data Analytics
Database Systems

Tools to build your Junior Analytics 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 Analytics Engineer | dbt & SQL | Building Reliable Data Models for 50+ Stakeholders

Analytics Engineer | Data Modeling & Pipelines | 35% Data Quality Improvement

Junior Analytics Engineer | dbt, Python, Snowflake | Self-Service Analytics

Analytics Engineer | Transforming Raw Data into Insights | Modern Data Stack

Junior Analytics Engineer | SQL, dbt, Airflow | Data Quality & Testing

Analytics Engineer | Building Analytics Infrastructure | Data Reliability

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

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Power Bullet Points

Adapt these achievement-focused bullets to showcase your impact.

Data Modeling & Transformation

• Built 15+ dbt data models transforming raw product and user data into clean dimensional models, enabling 50+ stakeholders to self-serve insights with <5-minute query times

• Developed fact and dimension tables using star schema design in Snowflake, organizing 5M+ records across 20+ tables for efficient querying and reporting

• Created reusable dbt macros and packages reducing code duplication by 40% and improving model build time from 45 minutes to 18 minutes

• Implemented incremental models for large event tables (100M+ rows), reducing refresh time from 8 hours to 45 minutes and cutting compute costs by 60%

Data Quality & Testing

• Implemented dbt data tests (uniqueness, not-null, relationships, accepted values) across 15 models, catching 25+ data quality issues before they reached production

• Built custom data validation framework detecting anomalies in key metrics, reducing data incidents by 35% and improving stakeholder trust in analytics

• Created data lineage documentation and ERDs in dbt, improving data discovery time by 50% and reducing "where does this data come from?" questions by 40%

• Set up data monitoring alerts in dbt Cloud and Monte Carlo, achieving 99.2% data pipeline SLA and reducing mean time to detection (MTTD) from 6 hours to 30 minutes

Analytics Infrastructure & Orchestration

• Developed data pipelines using Airflow orchestrating 10+ dbt jobs and Python scripts, automating daily data refreshes for 5 dashboards serving 100+ users

• Implemented CI/CD for dbt models using GitHub Actions, automating testing and deployment reducing model deployment time from 2 hours to 15 minutes

• Built metadata layer in dbt exposing business-friendly column descriptions and metric definitions, reducing analyst onboarding time by 30%

• Migrated legacy SQL queries to dbt models, improving code maintainability, version control, and reducing technical debt by 45%

Collaboration & Enablement

• Partnered with data analysts and business users to understand reporting needs, translating requirements into 12 dbt models and 8 Looker dashboards

• Conducted dbt training sessions for 8 analysts, enabling them to contribute SQL transformations and reducing analytics engineering backlog by 50%

• Documented data model logic, definitions, and SLAs in Confluence and dbt docs, improving data literacy and reducing support requests by 35%

• Participated in code reviews for 30+ dbt models, providing feedback on SQL optimization, naming conventions, and testing standards

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

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Resume Writing Tips for Junior Analytics Engineers

Emphasize dbt and Modern Data Stack

Analytics engineering is built on dbt. Highlight dbt proficiency: models built, tests written, documentation created, macros developed. Also show modern stack knowledge: Snowflake/BigQuery, Airflow/Dagster, Looker/Tableau, Git. This proves you understand the analytics engineer role.

Quantify Data Quality and Reliability

Don't just say "built data models"—quantify reliability. Include: data quality improvements (35% fewer issues), pipeline SLAs (99%+ uptime), MTTD (mean time to detection), test coverage (80%+ models tested). Show you care about trustworthy data, not just working pipelines.

Show SQL Expertise and Optimization

Analytics engineers live in SQL. Highlight: complex queries written, query performance improvements (8 hours → 45 minutes), incremental models, window functions, CTEs. Include specific optimizations: indexing, partitioning, materialized views. Demonstrate you write efficient, maintainable SQL.

Demonstrate Stakeholder Enablement

Analytics engineers enable self-service analytics. Include bullets about: stakeholders served (50+), documentation created, training provided, support requests reduced. Show you understand your job is making analysts and business users successful.

Balance Technical Depth with Business Context

List 10-15 skills covering transformation (dbt, SQL), programming (Python), warehouses (Snowflake, BigQuery), orchestration (Airflow), BI (Looker), and practices (testing, version control). Show you bridge data engineering and analytics—T-shaped with depth in transformation.

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Essential Skills & Keywords

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

Core Technologies

dbt (Data Build Tool) SQL (Advanced) Python Data Modeling Star Schema Design ETL/ELT Git & GitHub

Cloud Data Warehouses

Snowflake BigQuery Redshift Databricks SQL

Orchestration & Workflow

Apache Airflow dbt Cloud Dagster Prefect

BI & Visualization

Looker Tableau Power BI Metabase

Data Quality & Observability

dbt Tests Great Expectations Monte Carlo Data Lineage Data Documentation

Best Practices

Version Control (Git) CI/CD for Analytics Data Testing Code Reviews Agile/Scrum Dimensional Modeling

💡 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

dbt and data modeling expertise: Built 15+ dbt models with documentation—demonstrates core analytics engineering skill of transforming raw data into clean models
Data quality and testing: Improved data quality by 35%, implemented testing frameworks—shows attention to reliability beyond just building pipelines
SQL proficiency and stakeholder enablement: Serves 50+ stakeholders, complex SQL queries—analytics engineers bridge technical and business sides
Self-service analytics mindset: Built dashboards, documentation, semantic layers—enables analysts and business users to self-serve

✓ ATS-Friendly Elements

Entry-level keywords: "dbt," "SQL," "Python," "data modeling," "data pipelines," "analytics engineering"
Action verbs: Built, Developed, Implemented, Improved, Documented
Technologies: dbt, SQL, Python, Airflow, Snowflake, Git
Practices: data modeling, data testing, documentation, version control
Quantified contributions: models built, stakeholders served, data quality improvements

✓ Human-Readable Design

Summary emphasizes analytics engineering fundamentals: pipelines, modeling, data quality
Metrics scaled appropriately: 15 models, 50 stakeholders, 35% quality improvement
Experience shows progression from data analyst to analytics engineer
Skills balance SQL, Python, dbt, cloud warehouses, and BI tools
Recent degree or bootcamp in data/analytics signals entry level

💡 Key Takeaways

Junior analytics engineers should emphasize dbt, SQL, and data modeling fundamentals
Quantify your work: models built, stakeholders served, data quality improvements, pipeline reliability
Show technology breadth: dbt, SQL, Python, cloud warehouses, orchestration tools
Highlight data quality: testing, documentation, monitoring, incident response
Bridge technical and business: work with data engineers, analysts, and business stakeholders

📈 Career Progression in Analytics Engineering

See how Analytics Engineering roles evolve from data modeling to platform architecture.

📊 Junior Analytics Engineer (Current) ⚙️ Analytics Engineer → 🚀 Senior Analytics Engineer →

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Junior Analytics Engineer Resume Example