Senior Analytics Engineer Resume Example

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Senior Analytics Engineer Resume Sample

Casey Rivera
casey@rivera.dev
•
(415) 555-0410
•
linkedin.com/in/casey-rivera-analytics
•
github.com/caseyrivera
Senior Analytics Engineer
Senior Analytics Engineer with 8+ years leading data platform strategy and analytics engineering teams. Architected enterprise analytics platform serving 1,000+ users processing 5B+ records daily, reduced data infrastructure costs by 50%, and built analytics engineering practice from ground up. Expert in dbt, data architecture, and analytics platform leadership. Drive data strategy and organizational data maturity.
WORK EXPERIENCE
Senior Analytics Engineer
Jan 2021 – Present
Enterprise SaaS (Public Company)
  • Platform Architecture & Leadership: Architected enterprise analytics platform serving 1,000+ users processing 5B+ records daily, designed data mesh across 12 domains achieving 99.95% reliability and advancing data maturity from Level 2 to Level 4
  • Cost & Performance Optimization: Reduced data infrastructure costs by 50% ($1.8M annually) through Snowflake optimization and workload management while improving query performance by 70% and maintaining enterprise-scale reliability
  • Team Building & Organizational Impact: Built analytics engineering team from 2 to 10 engineers, mentored 8 engineers with 6 promotions, trained 100+ users on dbt, and drove 80% self-service analytics adoption
Analytics Engineer → Senior Analytics Engineer
Apr 2018 – Dec 2020
FinTech Unicorn (Series D)
  • Analytics Platform: Led migration from legacy ETL to modern dbt-based stack, built 150+ models serving 300+ stakeholders, reduced transformation code by 60% and improved development velocity by 5x
  • Data Quality Framework: Implemented comprehensive data testing and observability platform reducing data incidents by 70%, MTTD from 6 hours to 10 minutes, and achieving 99.8% data quality SLA
  • Technical Leadership: Established analytics engineering best practices, code review standards, and dbt style guide adopted across 20-person data team, reducing onboarding time by 50%
Analytics Engineer
Jun 2016 – Mar 2018
E-Commerce Startup
  • Data Modeling: Built foundational dbt models and dimensional data warehouse serving 50+ analysts and business users with self-service analytics capabilities
  • Pipeline Development: Developed Airflow orchestration for analytics workflows automating daily refreshes for 20+ dashboards with 99% reliability
  • Stakeholder Enablement: Trained analysts on SQL and dbt enabling them to build their own models, conducted office hours reducing analytics engineering support requests by 40%
SKILLS & COMPETENCIES
Enterprise Data Architecture | dbt (Expert) | SQL (Expert) | Python (Advanced) | Data Mesh | Snowflake (Expert) | BigQuery | Apache Airflow | Dimensional Modeling | Semantic Layer Design | Monte Carlo | Data Governance | Cost Optimization | Performance Engineering | Technical Leadership | Team Building | Mentorship | Data Strategy | Stakeholder Management
CERTIFICATIONS
dbt Analytics Engineering Certification
Feb 2022
dbt Labs
SnowPro Advanced: Architect
May 2022
Snowflake
EDUCATION
Master of Science in Data Science
2014-2016
Northwestern University
Evanston, Illinois
  • Data Engineering
  • Analytics

Tools to build your Senior 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.

Senior Analytics Engineer | Enterprise Data Platform | 1,000+ Users & 5B+ Records Daily
Senior Analytics Engineer | dbt & Modern Data Stack Leadership | 50% Cost Reduction
Analytics Engineering Leader | Building Data Platforms at Scale | 99.95% Reliability
Senior Analytics Engineer | Data Architecture & Strategy | Transforming Data Maturity
Staff Analytics Engineer Track | Platform Engineering & Team Leadership | Enterprise Scale
Senior Analytics Engineer | dbt Expert | Building Analytics Engineering Practice

💡 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.

Platform Architecture & Strategy

• Architected enterprise analytics platform serving 1,000+ users and processing 5B+ records daily, designing data mesh architecture across 12 domains achieving 99.95% reliability
• Drove data platform strategy and 3-year technical roadmap, evaluating and adopting modern data stack (dbt, Snowflake, Fivetran, Monte Carlo) saving $2M+ annually
• Designed semantic layer and metrics framework powering 200+ dashboards and 500+ metrics, establishing single source of truth reducing metric discrepancies by 90%
• Led migration from legacy Informatica ETL to modern dbt-based ELT, reducing transformation code by 60% and improving development velocity by 5x

Cost & Performance Optimization

• Reduced data infrastructure costs by 50% ($1.8M annually) through Snowflake optimization, query tuning, and workload management while improving performance by 70%
• Architected tiered storage and compute strategy (hot/warm/cold) for 10TB+ data warehouse, reducing storage costs by 40% while maintaining sub-3s query performance
• Implemented incremental models, clustering, and materialization strategies for billion-row tables reducing refresh time from 24 hours to 2 hours
• Established cost governance framework with query monitoring and warehouse rightsizing, preventing $500K+ in wasteful spend

Data Quality & Observability

• Built enterprise data quality framework with 500+ dbt tests, data contracts, and anomaly detection achieving 99.8% data quality SLA
• Implemented data observability platform (Monte Carlo, dbt exposures, custom monitors) reducing MTTD from 6 hours to 10 minutes and MTTR from 4 hours to 30 minutes
• Established data incident management process with SLAs, on-call rotation, and postmortems improving data reliability and stakeholder trust
• Created data lineage and impact analysis tooling enabling 95% of stakeholders to self-diagnose data issues, reducing support burden by 60%

Team Building & Leadership

• Built analytics engineering team from 2 to 10 engineers, establishing hiring, onboarding, and career progression framework with <10% attrition
• Mentored 8 analytics engineers advancing 4 from junior to mid-level and 2 to senior level through structured development and technical coaching
• Established analytics engineering best practices, style guides, and code review standards adopted across 40-person data organization
• Led quarterly data engineering guilds and internal conferences sharing knowledge on dbt, data modeling, and analytics engineering practices

Organizational Impact & Enablement

• Drove data maturity from Level 2 (Managed) to Level 4 (Optimized) measured by DMM framework, enabling self-service analytics adoption by 80% of organization
• Trained 100+ analysts, data scientists, and engineers on dbt, SQL optimization, and data modeling enabling 50% of new models to be built by non-AE teams
• Partnered with data engineering, analytics, and product to define data contracts and SLAs, reducing cross-team friction by 70%
• Presented data platform strategy to executive leadership and Board, securing $3M investment in data infrastructure and team growth

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

📝

Resume Writing Tips for Senior Analytics Engineers

1

Lead with Enterprise-Scale Platform Impact

Senior analytics engineers architect platforms for entire organizations. Lead with user scale (1,000+), data volume (5B+ records), cost impact ($1.8M saved), domains owned (12), reliability (99.95%). Show you think at enterprise scale—your decisions affect hundreds of stakeholders.

2

Demonstrate Strategic Technical Leadership

Include bullets on platform strategy, technical roadmaps, architecture decisions, technology evaluations. Show you drive long-term technical direction, not just execute plans. Mention: 3-year roadmap, data mesh design, modern stack adoption, migration leadership. Senior level means shaping the future.

3

Showcase Team Building and Organizational Impact

Senior engineers multiply impact through people. Quantify: team size grown (2→10), engineers mentored (8), promotions enabled (6), data maturity levels advanced (Level 2→4). Show you build capabilities, not just systems. Your impact is organizational transformation.

4

Balance Deep Technical Expertise with Business Value

Senior analytics engineers are both technical experts and business partners. Include deep technical details (incremental models, clustering, data mesh) balanced with business outcomes (cost savings, reliability, self-service adoption). Show you connect technical decisions to business value.

5

Position for Staff/Principal Track

List 15-20+ skills spanning architecture (data mesh, semantic layers), platforms (dbt at scale, Snowflake optimization), governance, leadership, and strategy. Show you're not just executing—you're defining the field. Include thought leadership, conference talks, open source contributions if applicable.

🎯

Essential Skills & Keywords

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

Platform Architecture

Enterprise Data Architecture Data Mesh Semantic Layer Design Metrics Framework Data Governance Platform Strategy

Core Technologies (Expert)

dbt (Expert) SQL (Expert) Python (Advanced) Data Modeling (Expert) Dimensional Modeling Data Vault

Cloud Data Platforms

Snowflake (Expert) BigQuery Databricks Redshift Fivetran Airbyte

Data Quality & Observability

Monte Carlo Great Expectations Data Contracts Anomaly Detection Data Lineage Incident Management

Performance & Optimization

Query Optimization Cost Optimization Workload Management Incremental Processing Clustering & Partitioning Materialization Strategies

Leadership & Strategy

Technical Leadership Team Building Mentorship Data Strategy Stakeholder Management Data Maturity Models

💡 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

  • Enterprise-scale platform architecture: Built platform serving 1,000 users, 5B records—senior scope means shaping entire organization's analytics capability
  • Strategic technical leadership: Drove data platform strategy, built AE practice from scratch—shapes long-term technical direction and organizational capability
  • Cost and scale optimization: 50% cost reduction, 5B records, enterprise reliability—demonstrates optimization at massive scale with business impact
  • Organizational influence: Built teams, established standards, drove data maturity—senior engineers multiply impact across entire organization

✓ ATS-Friendly Elements

  • Senior-level keywords: "data platform strategy," "analytics engineering leadership," "enterprise data architecture," "dbt," "data governance"
  • Strategic verbs: Architected, Led, Drove, Established, Built, Scaled
  • Business impact: cost reduction, user scale, data maturity, organizational capability
  • Technical depth: dbt at scale, data architecture, platform engineering, observability
  • 8+ years experience with clear progression to senior/staff level

✓ Human-Readable Design

  • Summary positions as platform leader: enterprise architecture, team building, strategic decisions
  • Metrics reflect senior scope: 1,000 users, 5B records, 50% cost savings, org-wide impact
  • Experience shows progression: Junior AE → AE → Senior AE → potential Staff/Principal
  • Demonstrates influence: shaped platform strategy, built teams, drove data maturity
  • Balance deep technical expertise with leadership, strategy, and organizational transformation

💡 Key Takeaways

  • Senior analytics engineers architect enterprise platforms and drive organizational data strategy
  • Quantify impact at scale: users served, records processed, cost savings, data maturity levels
  • Show technical leadership: drove architecture, established patterns, built teams, mentored engineers
  • Demonstrate platform thinking: scalability, reliability, governance, self-service at enterprise scale
  • Balance hands-on technical work with strategy, mentorship, and organizational influence

📈 Career Progression in Analytics Engineering

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

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