1Password

Senior Analytics Engineer

1Password1 weeks ago
Location

Remote (United States | Canada)

Workplace

Remote

Type

Full Time

Salary

USD 138,000 – 193,000

Level

Senior

Role

Data Engineer

Posted

Jul 15, 2026

Full TimeRemoteSenior

The role

Summary

Senior Analytics Engineer at 1Password is a pivotal role focused on designing, building, and maintaining scalable data infrastructure that powers trusted analytics, reporting, and AI-driven insights across the organization. This position bridges data engineering and analytics, requiring expert-level proficiency in SQL, dbt, and modern cloud data warehouses to transform complex, multi-source data into reliable, well-documented datasets serving Product, Finance, GTM, and Marketing teams. The ideal candidate brings 5+ years of analytics or data engineering experience with 3+ years in production analytics engineering, advanced modeling expertise, and a demonstrated ability to establish governance standards and mentor junior engineers while ensuring data quality, security, and performance at enterprise scale.

What you'll do

Design and own scalable data models: Architect and maintain production dbt models and datasets that serve as the authoritative single source of truth for critical business metrics across Finance, Product, GTM, and Marketing functions. Design clear grain hierarchies (dimensions, facts, timeseries, and marts) that enable downstream analytics tools and AI systems to operate with confidence and efficiency.
Establish data governance and semantic layer standards: Contribute to semantic layer implementation and metric governance frameworks, ensuring metric definitions remain consistent, well-documented, and reliable across all reporting surfaces. Champion dimensional modeling best practices and ensure business logic is transparent and accessible to both technical and non-technical stakeholders.
Drive team-wide standards and best practices: Define and implement organizational standards for dbt modeling patterns, testing frameworks, naming conventions, and CI/CD deployment practices. Lead adoption across the team through code reviews, documentation, and collaborative refinement to elevate overall data engineering maturity.
Implement data quality and observability: Design and deploy data quality frameworks, automated data validation, and observability strategies that surface data issues proactively. Build stakeholder confidence through transparent data lineage, metadata documentation, and monitoring that detects pipeline anomalies before they impact downstream analysis.
Optimize warehouse performance and efficiency: Collaborate with Data Infrastructure and Engineering teams to analyze query patterns, optimize model performance, and implement partitioning and incremental processing strategies. Continuously evaluate cloud data warehouse capabilities (Snowflake, BigQuery, Athena, Databricks, or Redshift) to maximize efficiency at organizational scale.
Evaluate and integrate analytics tooling: Research, evaluate, and introduce new data tooling and methodologies that improve reliability, scalability, and developer experience across the analytics stack. Stay current with emerging technologies in semantic layers, metrics platforms, reverse ETL, and data mesh architecture.
Ensure security and privacy compliance: Embed security and privacy-first principles into all data ingestion and modeling workflows, adhering to 1Password's rigorous information security standards. Implement access controls, data lineage tracking, and audit capabilities that support SOC 2 compliance and enterprise security requirements.
Translate business requirements into technical solutions: Partner with cross-functional stakeholders to understand ambiguous business requirements and scope them into well-defined technical solutions. Serve as a trusted advisor who bridges the gap between business objectives and technical implementation, articulating trade-offs and recommending data-driven solutions.
Mentor analytics engineering team members: Guide junior and intermediate analytics engineers through hands-on code review, collaborative pairing sessions, and knowledge-sharing initiatives. Foster a culture of technical excellence and continuous learning that accelerates team capability and retention.
Support AI and analytics workloads: Build and maintain data foundations that directly enable AI analytics capabilities, machine learning model development, and advanced statistical analysis. Collaborate with data science and product teams to ensure data pipelines meet the latency, accuracy, and completeness requirements of AI-driven systems.

What we look for

Technical

Advanced SQL proficiencyExpert-level SQL skills including complex window functions, CTEs, recursive queries, and query optimization techniques. Ability to write performant SQL at scale across cloud data warehouses and debug complex data transformations.
Production dbt expertise3+ years of hands-on production dbt development including advanced modeling patterns (staging, intermediate, marts), custom macros, tests, and incremental models. Deep familiarity with dbt project structure, version control integration, and multi-environment deployment pipelines.
Cloud data warehouse masteryExtensive production experience with modern cloud data warehouses such as Snowflake, BigQuery, AWS Athena, Databricks, or Redshift. Strong understanding of warehouse-specific optimization techniques, cost management, partitioning strategies, and columnar storage principles.
Dimensional modeling and metrics designExpert-level knowledge of dimensional modeling (Kimball methodology), fact and dimension table design, and grain specifications. Proficiency in defining metrics, aggregation hierarchies, and ensuring business logic consistency across analytics tools.
Semantic layer and metrics toolingHands-on experience with semantic layer platforms (LookML, MetricFlow, dbt Semantic Layer) or equivalent in-repository metric standards. Understanding of centralized metric definitions and how they integrate with downstream BI tools.
Data pipeline orchestrationPractical experience with orchestration and workflow tools such as Airflow, Dagster, or Prefect. Understanding of DAG concepts, error handling, retries, and monitoring in production data pipeline environments.
CI/CD for data pipelinesHands-on proficiency with CI/CD practices applied to data workflows, including automated testing, schema validation, data quality checks, and multi-environment promotion strategies. Experience with dbt Cloud, Git workflows, and automated deployment pipelines.
Data modeling patternsExpert understanding of advanced data modeling patterns including Slowly Changing Dimensions (SCD), snapshot tables, event stream modeling, and behavioral data transformation techniques.

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, or related fieldFormal education in quantitative disciplines. Candidates with equivalent professional experience and demonstrated expertise may be considered.
Data engineering or analytics certifications (preferred)Industry certifications such as dbt Fundamentals/Advanced, cloud data warehouse certifications (GCP Professional Data Engineer, AWS Certified Data Analytics, Azure Data Engineer Associate), or similar credentials are valued but not required.

Experience

5+ years in analytics or data engineeringBroad experience across data roles with demonstrated progression from junior to senior-level responsibilities. Exposure to end-to-end data pipeline development, analytics implementation, and data warehouse architecture.
3+ years focused on analytics engineeringSignificant hands-on experience specifically in analytics engineering roles, building production data models, and supporting business analytics and reporting requirements at scale. Proven track record of delivering high-impact data solutions.
B2B SaaS metrics experience (preferred)Experience modeling B2B SaaS metrics including subscription revenue, customer lifecycle analytics, usage and adoption patterns, and cohort analysis. Understanding of retention, expansion, and churn metrics in recurring revenue business models.
Event-stream and behavioral data modeling (preferred)Experience with event-driven data architectures, clickstream analysis, and behavioral data transformation. Familiarity with event streaming platforms and real-time data modeling patterns.
Lakehouse and advanced formats (preferred)Experience with lakehouse architectures, Apache Iceberg, Parquet formats, and merge-based incremental loading strategies. Understanding of open table formats and their advantages for analytics workloads.
Data mesh and reverse ETL (preferred)Familiarity with data mesh concepts, reverse ETL patterns, and operational analytics. Experience with data as a product and domain-driven analytics architecture.
Open-source contributions (preferred)Active participation in open-source data tools and frameworks demonstrates commitment to the broader analytics engineering community and deep technical expertise.

Skills

Required skills

SQLAdvanced SQL with window functions, CTEs, recursive queries, and query optimization for cloud data warehouses
dbt (data build tool)Production-grade dbt development including models, macros, tests, incremental processing, and multi-environment CI/CD
Cloud Data WarehousesDeep expertise in Snowflake, BigQuery, Athena, Databricks, or Redshift including performance tuning and cost optimization
Dimensional ModelingFact and dimension design, grain specification, Kimball methodology, and business logic documentation
Data Quality and TestingFramework design and implementation for data validation, anomaly detection, and monitoring using dbt tests and observability tools
Python or ScalaScripting and data transformation capabilities for complex logic beyond SQL, particularly for data pipeline development
Orchestration ToolsAirflow, Dagster, Prefect, or similar tools for building and managing production data workflows
Git and Version ControlProficiency with Git workflows, branching strategies, and code review practices for collaborative analytics engineering
CommunicationAbility to explain complex data concepts clearly to diverse audiences including analysts, product managers, and executives

Nice to have

Semantic Layer PlatformsLookML, MetricFlow, dbt Semantic Layer, or similar tools for centralized metric management
Reverse ETLExperience with tools and concepts for operational analytics and pushing data to downstream business applications
Apache Spark and LakehouseFamiliarity with distributed computing frameworks and lakehouse architectures (Iceberg, Delta Lake, Parquet)
Data Mesh ArchitectureUnderstanding of domain-driven data architecture, data as a product, and federated ownership models
Advanced AnalyticsExposure to statistical analysis, attribution modeling, experimental design, or other advanced analytics methodologies
API IntegrationExperience integrating third-party APIs and building data pipelines from SaaS applications
Cloud PlatformsAWS, GCP, or Azure services including IAM, storage, compute, and networking for analytics infrastructure
Open-Source ContributionsActive involvement in data tooling communities, dbt packages, or similar open-source projects

Compensation & benefits

Salary

USD 138,000 – 193,000 (annual)

Stock options

Available

Benefits

Equity compensation

RSU (Restricted Stock Unit) grant program for most employees, providing long-term wealth accumulation and alignment with company growth

Retirement planning

401(k) matching program in the US with competitive contribution matching to support long-term financial security

Health and wellness benefits

Comprehensive health, vision, and dental coverage with competitive plans to support overall wellbeing

Generous paid time off

Flexible PTO policy that respects work-life balance and enables meaningful time for rest, travel, and personal priorities

Parental leave programs

Enhanced maternity and parental leave top-up programs that exceed statutory minimums, supporting family planning

Free 1Password account

Complimentary access to premium 1Password account for personal use, providing security benefits and real-world product experience

Paid volunteer days

Dedicated paid time for employees to engage in community service and volunteer work supporting causes they care about

Peer recognition program

Bonusly peer-to-peer recognition platform enabling colleagues to acknowledge contributions and celebrate wins together

Remote-first work environment

Flexible work arrangement allowing employees to work from home while maintaining culture through periodic in-person team offsites and company events

Professional development

Support for continuous learning through training, conference attendance, and skill development aligned with career growth

Mental health support

Employee assistance programs and mental health resources supporting psychological wellbeing alongside physical health

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1Password

1Password

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1Password is a password manager that provides secure password storage and management solutions for individuals, families, and businesses.

Toronto, ON, CanadaFounded 20041password.com

Tech Stack

Languages
SQLPythonScala
Frameworks
dbt (data build tool)Apache SparkPandas/NumPy
Databases
SnowflakeGoogle BigQueryAWS AthenaDatabricksAmazon Redshift
Tools
AirflowDagsterPrefectdbt CloudLookMLMetricFlowGitGitHub or GitLab
Other
Data quality frameworksData observability platformsReverse ETLCloud platformsAPI integration patternsLakehouse formats
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