Analytics Engineer Resume Example

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Analytics Engineer Resume Sample

Taylor Morgan
taylor@morgan.dev
•
(650) 555-0395
•
linkedin.com/in/taylor-morgan-analytics
•
github.com/taylormorgan
Analytics Engineer
Analytics Engineer with 5 years building scalable data platforms and analytics infrastructure. Led transformation of data stack serving 200+ stakeholders, built 50+ dbt models processing 500M+ records, and improved data pipeline reliability to 99.8%. Expert in dbt, SQL, Python, and modern data warehouses. Drive data platform strategy and enable self-service analytics.
WORK EXPERIENCE
Analytics Engineer
Mar 2022 – Present
FinTech Unicorn (Series D)
  • Data Platform & Architecture: Led analytics infrastructure transformation serving 200+ stakeholders, migrating to dbt-based stack processing 500M+ records daily achieving 99.8% reliability and reducing data incidents by 65%
  • Performance & Cost Optimization: Optimized dbt models and Snowflake configuration reducing compute costs by 40% ($120K annually) while improving query performance by 60% and dashboard load times from 45s to <3s
  • Data Quality & Enablement: Built data observability platform and testing framework, trained 20+ analysts on dbt, reduced support tickets by 40% and enabled self-service analytics adoption across organization
Junior Analytics Engineer → Analytics Engineer
Jul 2019 – Feb 2022
E-Commerce Scale-up
  • Data Modeling: Built 50+ dbt models across product, marketing, and operations domains using dimensional modeling, enabling 100+ stakeholders to self-serve insights with consistent business logic
  • Pipeline Development: Developed Airflow orchestration for dbt jobs and Python scripts, automated analytics workflows reducing manual effort by 80% and achieving 99.5% pipeline SLA
  • Data Testing & Documentation: Implemented comprehensive dbt tests and documentation improving data quality by 45%, reducing "where does this data come from?" questions by 60%
SKILLS & COMPETENCIES
dbt (Advanced) | SQL (Expert) | Python | Snowflake (Advanced) | Apache Airflow | Data Architecture | Dimensional Modeling | Data Mesh | Looker (LookML) | Monte Carlo | dbt Tests & Macros | Git & CI/CD | Performance Optimization | Cost Optimization | Data Governance | Stakeholder Enablement
CERTIFICATIONS
dbt Analytics Engineering Certification
Jun 2023
dbt Labs
SnowPro Core Certification
Sep 2023
Snowflake
EDUCATION
Bachelor of Science in Information Systems
2015-2019
University of Texas at Austin
Austin, Texas
  • Data Analytics
  • Business Intelligence

Tools to build your Analytics Engineer resume

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

✨

Resume Headlines

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

Analytics Engineer | dbt & Modern Data Stack | Serving 200+ Stakeholders with 99.8% Reliability
Analytics Engineer | Data Platform & Architecture | 50+ dbt Models Processing 500M+ Records
Mid-Level Analytics Engineer | dbt, Snowflake, Airflow | Self-Service Analytics at Scale
Analytics Engineer | Building Scalable Data Infrastructure | Data Quality & Performance
Analytics Engineer | Modern Data Stack Expert | 40% Cost Reduction & 99.8% Uptime
Analytics Engineer | Data Modeling & Transformation | Enabling Data-Driven Decisions

💡 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 Platform & Architecture

• Led transformation of analytics infrastructure serving 200+ stakeholders, migrating legacy ETL to modern dbt-based ELT stack processing 500M+ records daily with 99.8% reliability
• Architected dimensional data models across 5 business domains (product, marketing, finance, operations, customer), establishing star schema patterns adopted org-wide
• Built semantic layer in dbt exposing 100+ metrics with consistent business logic, reducing "metric definition confusion" by 70% and improving decision-making consistency
• Designed data mesh architecture enabling 3 domain teams to own their analytics models, improving delivery velocity by 50% while maintaining governance standards

Performance & Optimization

• Optimized dbt models and Snowflake warehouse configuration reducing compute costs by 40% ($120K annually) while improving query performance by 60%
• Implemented incremental models, clustering, and partitioning strategies for large tables (1B+ rows), reducing refresh time from 12 hours to 90 minutes
• Built materialized views and aggregation tables for high-traffic dashboards, improving dashboard load time from 45s to <3s serving 500+ daily users
• Established dbt model performance benchmarks and SLAs, proactively identifying and resolving slow queries before they impact stakeholders

Data Quality & Observability

• Implemented comprehensive data testing framework with 200+ dbt tests, custom SQL checks, and anomaly detection reducing data incidents by 65%
• Built data observability platform using Monte Carlo and dbt exposures, achieving 99.8% pipeline SLA and reducing MTTD from 4 hours to 15 minutes
• Created data quality dashboards tracking test failures, freshness, and completeness across 50+ models, improving data trust score from 6.2 to 8.7/10
• Established incident response playbooks and on-call rotation, resolving 95% of data issues within 1-hour SLA and improving stakeholder satisfaction

Enablement & Collaboration

• Trained 20+ analysts and data scientists on dbt, SQL best practices, and data modeling, enabling them to contribute 30% of new models
• Built internal dbt style guide, macros library, and starter templates reducing new model development time by 45% and improving code consistency
• Partnered with data engineering to establish SLAs for raw data ingestion, reducing analytics pipeline failures caused by upstream issues by 50%
• Led weekly office hours and data platform demos, reducing analytics engineering support tickets by 40% and improving self-service adoption

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

📝

Resume Writing Tips for Analytics Engineers

1

Lead with Platform-Level Impact

Mid-level analytics engineers own platforms, not just models. Lead with scope: stakeholders served (200+), data volume (500M+ records), domains owned (5), reliability (99.8%). Show you think beyond individual tasks—you build systems that scale.

2

Show Architectural and Strategic Thinking

Include bullets on data architecture, modeling standards, semantic layers, data mesh. Demonstrate you make architectural decisions: star schema, incremental models, materialization strategies. Mid-level means you shape how the team builds, not just what you build.

3

Quantify Performance and Cost Optimization

Analytics engineers at this level optimize for scale and cost. Include: cost reductions (40%, $120K saved), performance improvements (12 hours → 90 minutes), query optimization (45s → 3s). Show you balance functionality with efficiency.

4

Demonstrate Enablement and Multiplier Effect

Mid-level means multiplying your impact. Include: analysts trained (20+), self-service adoption, support tickets reduced (40%), knowledge sharing. Show you enable others—your impact is measured by what the team accomplishes, not just what you personally build.

5

Balance Technical Depth with Business Outcomes

List 12-18 skills covering transformation (dbt, SQL), architecture (dimensional modeling, data mesh), orchestration (Airflow), warehouses (Snowflake), observability (Monte Carlo), and governance. Show you're an expert practitioner who drives business outcomes.

🎯

Essential Skills & Keywords

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

Core Technologies

dbt (Advanced) SQL (Expert) Python Data Architecture Dimensional Modeling Data Mesh Git & CI/CD

Cloud Data Platforms

Snowflake (Advanced) BigQuery Databricks Redshift

Orchestration & Workflow

Apache Airflow dbt Cloud Dagster Prefect

Data Quality & Observability

Monte Carlo Great Expectations dbt Tests (Advanced) Data Lineage Anomaly Detection Data Cataloging

BI & Metrics

Looker (LookML) Tableau Semantic Layer Design Metrics Framework

Best Practices

Data Governance Performance Optimization Cost Optimization Incident Response Documentation Stakeholder Enablement

💡 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

  • Platform-level data transformation: Built 50+ models serving 200+ stakeholders, 500M records—mid-level scope includes owning data domains and platforms
  • Data architecture and strategy: Led data stack transformation, established modeling standards—owns architectural decisions beyond individual models
  • Scale and reliability: 99.8% pipeline reliability, cost optimization—demonstrates ability to build production-grade, scalable systems
  • Cross-functional leadership: Serves 200+ stakeholders, drives self-service—leads data platform strategy across organization

✓ ATS-Friendly Elements

  • Mid-level keywords: "analytics platform," "data architecture," "dbt," "data modeling," "pipeline reliability"
  • Strategic verbs: Led, Architected, Established, Optimized, Drove
  • Business impact: stakeholder scale, cost savings, data quality, self-service enablement
  • Technical depth: dbt, dimensional modeling, orchestration, data testing, observability
  • 5+ years experience with clear platform ownership

✓ Human-Readable Design

  • Summary positions as platform builder: data stack, 200+ stakeholders, analytics infrastructure
  • Metrics reflect mid-level scope: 50 models, 500M records, 200 users, org-wide impact
  • Experience shows progression: Data Analyst → Junior AE → Analytics Engineer
  • Demonstrates ownership: led transformations, established standards, drove strategy
  • Balance hands-on modeling with architecture, optimization, and enablement

💡 Key Takeaways

  • Mid-level analytics engineers own data domains and drive platform strategy, not just build models
  • Quantify platform impact: stakeholders served, records processed, cost savings, reliability metrics
  • Show architectural thinking: data architecture, modeling standards, observability, scalability
  • Demonstrate systems ownership: pipeline reliability, performance optimization, data governance
  • Balance technical execution with strategy, enablement, and cross-functional collaboration

📈 Career Progression in Analytics Engineering

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

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