Title
Analytics Engineer
Quick Summary
Meridian Metrics is hiring an Analytics Engineer to transform raw event data into clean, tested, and well-documented models that power dashboards, experiments, and product decisions. You will own the dbt codebase, define source-of-truth metrics with stakeholders, and ensure reliability through automated testing and observability. We welcome strong graduates and early-career professionals with practical SQL skills and a passion for clear data storytelling.
Project Category or Industry
Analytics engineering for SaaS and digital products.
Type
Full-time employment.
Experience Level
Entry to mid-level, with mentorship and clear growth paths for freshers; experienced candidates are also encouraged.
Duration
Permanent role.
Location
Remote-first with optional hybrid hubs in London and Warsaw. Maintain at least four hours of overlap between UTC−1 and UTC+3.
Salary
USD 88,000–128,000 base depending on location and experience, plus benefits and an annual performance bonus.
Payment Mode
Monthly payroll where supported; compliant contractor arrangements are available in select countries.
Hiring Company Name
Meridian Metrics
Required Skills or Tools
Strong SQL and comfort with data modeling; hands-on experience with dbt for transformations and testing; familiarity with a modern warehouse (Snowflake, BigQuery, or Redshift) and version control; ability to collaborate with analysts and product teams and to document decisions with clarity.
Project Description
Meridian Metrics builds the data layer that turns clickstream and application events into decision-ready models. As an Analytics Engineer, you will partner with product managers, analysts, and data engineers to define metrics, design dimensional models, and implement robust transformations with automated tests. Your work ensures stakeholders can trust dashboards and experiment readouts, while keeping costs and latency under control.
Core Responsibilities and Expected Deliverables
Design and maintain dimensional models and marts optimized for BI, experimentation, and self-serve analysis.
Build and refactor dbt projects with source freshness checks, schema tests, and data quality gates; manage environments and release processes.
Define and document canonical metrics and business logic; publish data dictionaries and change logs.
Collaborate on event instrumentation standards and data contracts with upstream teams; review tracking specs before launch.
Create dashboards and operational monitors that surface model health, freshness, and usage; provide runbooks for incident response.
Partner with analysts to scope questions, validate results, and enable self-service through semantic layers.
Required Experience and Preferred Qualifications
Proficiency in SQL and practical Python for utilities or lightweight transformations; sound engineering practices including code review and CI.
Experience with dbt Core or dbt Cloud, plus a warehouse such as Snowflake, BigQuery, or Redshift.
Working knowledge of BI platforms (Looker, Tableau, or Power BI) and semantic/metrics layers.
Preferred: familiarity with event schemas, Amundsen or DataHub for cataloging, Great Expectations or dbt tests for validation, and cost/performance tuning in cloud warehouses.
Evidence of impact through internships, open-source contributions, coursework, or shipped analytics projects will be valued.
Tools or Platforms to Be Used
Transformations and testing: dbt, dbt tests, Great Expectations where appropriate.
Warehousing and storage: Snowflake or BigQuery, object storage on S3 or GCS.
Orchestration and CI/CD: Airflow or Dagster, GitHub Actions.
BI and metrics: Looker or Tableau; optional metrics layer for consistent definitions.
Observability and catalog: OpenLineage/Marquez, DataHub, Prometheus, Grafana.
Language Requirement
Professional English is required. Additional languages are welcome for cross-regional collaboration.
Communication Style
Written-first collaboration using design docs and pull requests on GitHub; Slack for daily coordination; Zoom for stand-ups, reviews, and stakeholder readouts. Clear, accessible documentation is expected for all changes.
Time Commitment or Working Window
Standard 40 hours per week with flexible scheduling. Maintain a predictable daily block overlapping at least four hours with the core team between 09:00 and 17:00 in your local time.
Payment Terms
Salary is paid monthly via payroll. For contractors, invoices are processed on net-30 terms upon acceptance of deliverables and timesheets.
Evaluation Criteria
Portfolio or code samples demonstrating data modeling, dbt craftsmanship, and testing discipline.
Practical exercise building a small star schema with dbt tests, documentation, and a rollout plan.
Technical interview on dimensional modeling, metrics governance, warehouse performance, and cost optimization.
Final conversation on collaboration, communication, and product sense.
References may be requested.
Other Requirements
New hires sign a confidentiality agreement and follow security and data-handling policies. Light time-tracking may be used for distributed coordination. Occasional on-call for analytics model incidents is shared across the team.
About Meridian Metrics
Meridian Metrics is a privately held analytics engineering company helping product-led teams ship trustworthy metrics at speed. Based in London with a distributed team across Europe and North America, we combine rigorous modeling with pragmatic operations to support experimentation, growth, and data-driven product development. Learn more at https://www.meridianmetrics.co and reach our hiring team at careers@meridianmetrics.co.