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# Data Analytics Engineer

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/ruby-labs.md)Share21 Aug 2026Published63Listing views4Application actions21 Sep 2026Apply before  Opportunity details

## About this role.

AI SummaryRuby Labs is seeking a remote Data Analytics Engineer to build and maintain the analytical data layer used for company-wide reporting and decision-making. The role centers on developing dbt models, scalable SQL transformations, data-quality testing, documentation, and performant warehouse queries in BigQuery or ClickHouse. This person will partner closely with data engineering, analytics, product, marketing, and other business teams to turn operational requirements into reusable and governed data models. The position is suitable for a detail-oriented analytics engineer who is comfortable in a fast-moving environment, can independently resolve data reliability issues, and can collaborate within approximately four hours of CET.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

4/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

4/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThe role requires strong hands-on capability in SQL, dbt, analytical modeling, data quality, and cloud warehouse optimization, alongside the ability to translate ambiguous business processes into durable models. Its stated fast-growth culture and broad ownership across modeling, governance, monitoring, and stakeholder support indicate a demanding pace.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive$125,000US market range$100k–$150k0$165k

AI insightNo salary range was provided. For the U.S. market, a Data Analytics Engineer with dbt, advanced SQL, cloud warehouse, data-quality, and cross-functional modeling responsibilities typically commands approximately $100,000 to $150,000 annually, with an estimated median of $125,000 USD; contractor and non-U.S. location arrangements may vary materially.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Data Analytics Engineering](https://jobicy.com/jobs?search_keywords=Data%20Analytics%20Engineering.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Advanced SQL](https://jobicy.com/jobs?search_keywords=Advanced%20SQL.md)[BigQuery](https://jobicy.com/jobs?search_keywords=BigQuery.md)[ClickHouse](https://jobicy.com/jobs?search_keywords=ClickHouse.md)[Data Modeling](https://jobicy.com/jobs?search_keywords=Data%20Modeling.md)[Data Quality](https://jobicy.com/jobs?search_keywords=Data%20Quality.md)[ELT/ETL](https://jobicy.com/jobs?search_keywords=ELTETL.md)[Data Governance](https://jobicy.com/jobs?search_keywords=Data%20Governance.md)[Cloud Data Warehousing](https://jobicy.com/jobs?search_keywords=Cloud%20Data%20Warehousing.md)

Sample interview questionsHow would you structure a dbt project for a growing organization with multiple source systems?I would organize models into clear layers, typically staging, intermediate or core, and marts. Staging models standardize source data, core models apply reusable business logic and joins, and marts provide subject-area datasets for analytics. I would also establish naming conventions, source freshness checks, tests, documentation, and ownership standards from the outset.

What steps would you take to investigate inconsistent numbers between two dashboards?

I would first identify the exact metrics, filters, grain, reporting period, and source models used by each dashboard. I would trace lineage through the warehouse and dbt models, compare transformation logic and joins, then validate source-data completeness and freshness. After identifying the cause, I would correct the logic or definitions, add a regression test where possible, and document the agreed metric definition.

How do you optimize SQL transformations in BigQuery or ClickHouse?

I start by reviewing query plans, data volumes, join patterns, partitioning or clustering strategy, and unnecessary scans. In BigQuery, I would limit selected columns, use partition filters, materialize costly reusable transformations when appropriate, and monitor bytes processed. In ClickHouse, I would consider table engines, sort keys, partition keys, pre-aggregation, and query access patterns.

Describe a data-quality framework you would implement with dbt.

I would combine standard dbt tests such as unique, not_null, accepted_values, and relationships with custom tests for business rules, reconciliation, freshness, and volume anomalies. Critical models would have severity levels and clear alert ownership. I would monitor failures over time and prioritize prevention by validating data as close to ingestion and transformation boundaries as possible.

How do you gather requirements from product or marketing stakeholders for a new analytical dataset?

I clarify the business decision the dataset must support, the intended users, key entities, grain, definitions, time logic, filters, and expected refresh cadence. I translate these needs into a proposed model and metric contract, validate it with stakeholders using sample outputs, and document assumptions before implementation. After launch, I collect feedback and manage changes through a transparent versioning and communication process.

## About us

Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we’re always looking for passionate individuals to join us. Learn more about our story at: [https://rubylabs.com/about-us/](https://rubylabs.com/about-us/)

## About the role

We are looking for a Data Analytics Engineer to build, optimize, and maintain the analytical data layer that powers decision-making across the organization. You will transform business requirements into reliable, well-modeled datasets using dbt, SQL, and cloud data warehouses (BigQuery, ClickHouse). This role combines data modeling, documentation, data quality design, and close collaboration with analysts, engineers, and product teams to ensure consistent, trustworthy reporting.

You will serve as the bridge between raw data ingestion and analytics — shaping how data is structured, validated, and delivered.

Responsibilities

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Design, build, and maintain dbt models (staging, core, marts) based on business needs

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Develop scalable SQL transformations and ensure efficient, cost-effective query performance

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Implement data quality tests (dbt tests, custom checks, anomaly detection) and maintain high data reliability.

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Create and maintain documentation for datasets, data lineage, business logic, and transformation rules

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Collaborate with Data Engineers on improving raw data structures, ingestion patterns, and source readiness

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Partner with Data Analysts, Product Managers, and Marketing teams to translate business requirements into clean, reusable data models

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Monitor data pipelines and marts, resolve issues, and prevent inconsistencies in reporting

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Support governance initiatives: naming conventions, taxonomy, schemas, metric definitions, and standardization

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Contribute to continuous improvement of our data modeling frameworks, performance tuning, and technical decision-making

Requirements

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Strong SQL experience with a deep understanding of analytical modeling concepts

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Hands-on experience with dbt (models, tests, docs, macros)

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Experience working with cloud data warehouses such as BigQuery or ClickHouse

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Understanding of data modeling patterns

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Familiarity with data engineering concepts (ELT/ETL, orchestration, version control, CI/CD)

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Experience with data quality frameworks or implementing validation logic

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Ability to translate complex business processes into clean analytical datasets.

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Strong documentation skills and attention to detail

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Experience collaborating with cross-functional teams and supporting analysts with reliable data

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Knowledge of Python is a plus (but not required)

Nice To Haves

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Experience with orchestration tools (Airflow, Dagster)

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Experience working with marketing, product, or billing datasets

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Exposure to metric layers, semantic layer design, or BI governance

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Basic understanding of observability tooling (Grafana, Prometheus, … )

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Experience optimizing BigQuery costs or ClickHouse performance

## Location

Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours.

## Benefits

Discover the perks of being part of our vibrant team! We offer:

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Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance.

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Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days.

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Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge.

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Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them.

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Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: [https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt](https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0#heading=h.ndsdl4wapxtt)

Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth!

## Interview Process

After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps:

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Recruiter Screening (40 minutes)

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Technical Interview (90 minutes)

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Final Interview (60 minutes)

## Life at Ruby Labs

At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day.

This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen.

Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success.

#LI-Remote

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[Apply now >](https://jobicy.com/jobs/151318-data-analytics-engineer.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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