Analytics Engineers for Trusted Metrics

Hire Analytics Engineers
Who Turn Raw Data Into Decision Systems

Hire Analytics Engineers who turn warehouse data into trusted metrics, semantic models, BI layers, scorecards, and decision systems. Give leadership, product, finance, sales, and AI workflows one governed definition of the business.

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Senior Analytics Engineer

dbt Looker BigQuery Semantic Layer
All Levels

$4,800/mo

Junior from $2,400/mo · Mid from $3,500/mo · Senior from $4,800/mo

7-Day Risk-Free Trial

Zero commitment start

Onboard in 48 Hours

Pre-vetted, ready to ship

AI-Native Development

Faster iteration, cleaner code

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

Trusted by CTOs, Engineering Leaders & Operators Worldwide

10+ Years in Business

500+ Projects Delivered

200+ Global Clients

4.9/5 Client Satisfaction

Why Companies Struggle to Hire Analytics Engineers

Analytics engineering is where raw data becomes business language. Weak metric models create arguments, duplicated dashboards, inconsistent AI context, and leadership meetings where teams debate definitions instead of decisions.

The Hiring Problem

Teams argue over ARR, activation, retention, churn, pipeline, conversion, and usage definitions in every report

Dashboards are slow, duplicated, filtered differently, or built on fragile SQL that only one analyst understands

Analysts spend too much time cleaning data by hand because dbt models, tests, and ownership are incomplete

Executives, product teams, and AI workflows lose confidence because the semantic layer does not match how the business actually operates

Our Solution

Engineers build tested dbt models with clear grain, lineage, documentation, source freshness, and ownership

Trusted metrics are defined for revenue, retention, activation, funnel, cohorts, pipeline, usage, margins, and operations

Semantic layers improve in LookML, dbt Semantic Layer, MetricFlow, Tableau, Power BI, Mode, Metabase, or custom metric APIs

Data quality checks, metric reviews, dashboard certification, and governance protect reporting from broken source data

Why Hire Analytics Engineers from Devlyn

Senior, product-minded Analytics Engineers vetted for metric judgment, dbt modeling, BI usability, stakeholder communication, warehouse performance, and ownership after launch.

Why Hire Analytics Engineers from Devlyn
Metric Modeling

Metric Modeling

Defines consistent business metrics with clear grain, filters, joins, dimensional context, edge cases, time windows, and calculation logic.

dbt Transformation

dbt Transformation

Builds modular, tested, documented dbt models with source freshness, lineage, contracts, exposures, and reviewable SQL.

Semantic Layer Design

Semantic Layer Design

Creates governed dimensions, measures, entities, joins, and metric definitions for self-serve reporting and consistent AI context.

BI Enablement

BI Enablement

Supports Looker, Tableau, Power BI, Mode, Metabase, Sigma, Hex, and internal dashboards with clean source models.

Data Quality Testing

Data Quality Testing

Adds freshness, uniqueness, accepted value, relationship, not-null, volume, reconciliation, and custom business-rule tests.

Stakeholder-Ready Reporting

Stakeholder-Ready Reporting

Turns scattered requests into reliable dashboards, scorecards, definitions, and enablement for product, finance, sales, marketing, support, and operations.

How hiring actually works.

No procurement cycle, no mystery shortlists. Six steps from first call to first shipped feature, with timelines you can defend to leadership.

A 30-minute call to map the metrics people debate, current warehouse, dbt project, semantic layer, BI tools, reporting pain, source systems, security constraints, timezone overlap, and why the Analytics Engineer role is the right hire. If the real gap is data engineering, BI design, data science, RevOps, or a pod, we say that before you interview anyone.
Analytics Engineer Scoping Call
Within 24 hours, you receive pre-vetted Analytics Engineer profiles matched against your analytics surface: dbt, Looker, Tableau, Power BI, semantic layer design, executive KPI reporting, product analytics, revenue analytics, experimentation, and dashboard cleanup. Each profile includes technical context, availability, communication fit, and why the engineer belongs in your interview loop.
Analytics Engineer Shortlist
Use the interview loop to test semantic modeling, dbt patterns, metric definitions, dashboard trust, warehouse performance, stakeholder communication, and ability to explain tradeoffs to finance, product, and executive teams. You can run system design, a model review, a metric-definition exercise, or a paid task based on your real work.
Interview for Analytics Engineer Fit
NDA and IP assignment are completed first. Then we set up warehouse schemas, dbt project, BI dashboards, semantic definitions, metric ownership, data quality checks, source documentation, and the first reporting gap to fix so the engineer can contribute without a week of hand-holding.
Onboard Into the Analytics Engineer Workflow
By day 7, you should see a concrete proof point: a trusted metric clarified, a dbt model refactored, a dashboard query fixed, a test added, a semantic definition documented, a stale report retired, or a data-quality risk surfaced. Progress is visible before the trial becomes a long commitment.
First Analytics Engineer Proof Point
During the risk-free trial, you evaluate metric judgment, modeling discipline, SQL clarity, BI usability, communication clarity, and ability to make reporting reliable for leadership decisions. If the fit is wrong, we replace the engineer within 48 hours.
Analytics Engineer Trial Check

Analytics Engineer: Engagement Options

Three transparent ways to engage. All rates are in USD and exclude taxes. No recruitment fees, no notice periods.

Audit

dbt Project Audit & Refactor Plan

$9,500

fixed

2 weeks, senior analytics engineer

  • →Current-state audit
  • →Refactor plan
  • →Quick-win refactor PR
  • →Documentation pass
Talk to Sales

Embedded

Senior Analytics Engineer

$4,200

/mo

Full-time, 5–10+ years

  • →Owns metrics + semantic layer
  • →dbt project healthy
  • →BI + AI both consume same truth
  • →Reviews internal analysts
Talk to Sales

Analytics Pod

Analytics Eng + BI Engineer

$7,800

/mo

Pair build, 3–6 months

  • →End-to-end metrics platform
  • →dbt + semantic layer + BI
  • →Self-serve enablement
  • →Documentation + training
Talk to Sales

Where Analytics Engineers Create Leverage

From SMEs and scaling companies to enterprise teams. Same senior bar; different shape of engagement.

01.

Executive KPI Reporting

Create trusted scorecards for ARR, revenue, growth, retention, churn, funnel, margin, pipeline, customer health, and operational performance.

02.

Product Analytics Foundation

Model events, accounts, users, workspaces, cohorts, activation, engagement, retention, feature usage, experiments, and lifecycle stages consistently.

03.

Revenue Analytics

Unify CRM, billing, subscription, pipeline, product usage, support, contracts, and customer data into decision-ready revenue models.

04.

BI Cleanup

Reduce duplicate dashboards, inconsistent SQL, slow queries, unused reports, conflicting filters, and unclear data ownership.

What should change after you hire Analytics Engineers

A CTO is not hiring Analytics Engineers for activity, resumes, or another vendor dashboard. The hire has to create a visible business outcome, reduce delivery risk, and leave your internal team with a stronger system than before. This section defines the outcome we expect the engagement to prove.

Outcome 01 Trusted metrics that survive leadership review
+

The first meaningful outcome is a governed analytics layer that turns warehouse data into business definitions people can use. That may be an executive KPI scorecard, product analytics foundation, revenue analytics model, semantic layer, certified BI dashboard, or dbt refactor that makes a disputed metric clear. The Analytics Engineer should define the grain, filters, joins, time windows, owner, lineage, tests, documentation, and dashboard behavior so finance, product, sales, operations, and leadership stop rebuilding the same metric differently.

Evidence to expect: a trusted metric or model improvement with tests, definition notes, dashboard impact, lineage context, and data-quality risks

Outcome 02 Metric disputes are resolved before scale
+

The biggest Analytics Engineer hiring risk is a reporting environment where every dashboard is technically correct but business-wrong. Risks include mixed grains, double-counted revenue, inconsistent date logic, hidden filters, stale models, broken joins, ambiguous cohorts, untested assumptions, dashboard sprawl, and ownership gaps. We reduce that risk with dbt tests, source freshness, semantic definitions, metric review, certified dashboards, lineage documentation, reconciliation checks, and clear data owners.

Evidence to expect: known metric disputes, model tradeoffs, failing examples, test coverage, dashboard decisions, and a next-decision list stakeholders can inspect

Outcome 03 Analytics quality metrics a CTO can inspect
+

The engagement should be judged by metric consistency, certified dashboard adoption, dbt test coverage, model freshness, source freshness, failed test trend, query performance, dashboard load time, duplicate dashboard reduction, ad hoc request reduction, stakeholder satisfaction, and the number of core metrics with owners and documented definitions. These signals show whether reporting is becoming a trusted operating system or just another backlog.

Evidence to expect: a metrics-governance snapshot with baseline, model changes, test results, dashboard impact, and a recommendation on what to fix next

Outcome 04 Analytics practice your team keeps
+

A strong engagement should leave behind reusable analytics assets, not only dashboards. That includes metric definitions, event taxonomy notes, dbt model docs, semantic-layer conventions, dashboard certification rules, stakeholder review cadence, test conventions, source ownership, performance tuning notes, and enablement material for analysts and business users.

Evidence to expect: metric docs, dbt documentation, semantic conventions, dashboard ownership, decision records, runbooks, and handover material your team can maintain

How to decide if Devlyn is the right partner for Analytics Engineers

Choose us when

You need an Analytics Engineer who can join a live product, work with your existing team, and create a specific outcome without months of recruiting or unmanaged freelance risk.

Interview for

Use the interview to test semantic modeling, dbt patterns, metric definitions, dashboard trust, warehouse performance, and stakeholder communication. Ask how the engineer would define ARR, handle slowly changing dimensions, model activation, reconcile billing with CRM data, certify a dashboard, and prove that fewer people are asking for manual reports.

Expect clarity on

Scope, ownership, review cadence, communication rhythm, source-code access, warehouse access, BI access, metric owners, IP assignment, security constraints, timezone overlap, and what proof should exist by day 7.

Do not accept

A generic shortlist, vague seniority claims, unclear pricing, weak code review process, or a vendor who cannot explain how the Analytics Engineer scope will be governed after onboarding.

Delivery governance and risk control

Devlyn is positioned as a senior AI and software engineering partner, not a resume marketplace. You get structured onboarding, secure access, NDA and IP assignment support, communication overlap, replacement flexibility, and delivery governance built around the outcome you are hiring for.

For an Analytics Engineer engagement, governance means metric definitions, dbt models, data tests, dashboard ownership, semantic-layer changes, and documentation stay maintained. Your team should know who owns each metric, which dashboards are certified, which tests block release, which source systems are authoritative, and how changes to definitions are reviewed before leadership reporting changes.

We also align analytics work with AI readiness where relevant. If AI assistants, executive agents, or decision-support workflows consume company metrics, those systems need the same governed definitions, lineage, and freshness expectations as human-facing dashboards.

Ready to Hire an Analytics Engineer?

Share the metrics people argue about, the BI stack you use, and the decisions reporting must support. We will shortlist analytics engineers who can make reporting trustworthy.

Hire Analytics Engineer Now Book a Free Consultation

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Frequently Asked Questions

Answers for CTOs, engineering leaders, product leaders, operators, and hiring managers comparing senior engineering capacity, delivery models, risk controls, and long-term ownership.

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your warehouse, dbt project, BI tools, disputed metrics, timeline, and seniority needs. The goal is not to send resumes quickly; it is to send Analytics Engineers who match the outcome, risk profile, and communication bar for the role.

Yes. You interview the shortlisted engineers before committing. We recommend using the interview to test semantic modeling, dbt patterns, metric definitions, dashboard trust, warehouse performance, BI usability, and stakeholder communication. That makes the selection practical for a CTO instead of resume-led.

The first week should produce visible proof that the engineer understands your system and can move real work forward. For this role, you should see a trusted metric clarified, a dbt model refactored, a dashboard query fixed, a test added, a semantic definition documented, a stale report retired, or a data-quality risk surfaced. If progress is unclear, you should know that early, not after a long contract cycle.

A strong hire should produce trusted analytics models, semantic definitions, tests, documentation, and dashboards that leadership can use without second-guessing. The outcome should be measurable through metric consistency, certified dashboard adoption, dbt test coverage, model freshness, failed test trend, query performance, duplicate dashboard reduction, stakeholder adoption, and reduced ad hoc reporting.

Quality is managed through senior screening, role-specific interview criteria, code or architecture review, documented decisions, and delivery checkpoints. For Analytics Engineer work, we look for evidence across metric modeling, dbt transformations, semantic-layer design, BI enablement, data quality testing, stakeholder-ready reporting, warehouse performance, documentation, and communication with finance, product, sales, and operations leaders.

Yes. The engineer joins your tools, repositories, standups, issue trackers, review process, warehouse, dbt project, BI tools, stakeholder channels, and communication rhythm. For Analytics Engineer work, we define the operating model explicitly: metric definitions, dbt models, data tests, dashboard ownership, and documentation stay maintained.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, reviews, and escalation. For Analytics Engineer engagements, the communication rhythm is tied to the proof points that matter: metric consistency, dashboard trust, test coverage, model freshness, stakeholder adoption, and reduced ad hoc reporting.

NDA and IP assignment are handled before onboarding. Access is scoped to the tools, repositories, datasets, systems, or environments required for the Analytics Engineer scope, and sensitive work is governed through your security rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer can handle semantic modeling, dbt patterns, metric definitions, dashboard trust, warehouse performance, BI usability, and stakeholder communication. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one specialist, add adjacent roles, or move into a pod model depending on the scope. Common expansion paths include data engineering for pipeline reliability, BI engineering for dashboard design, RevOps for revenue definitions, product analytics for event instrumentation, and data governance for access and ownership.

Typical options include dbt Project Audit & Refactor Plan ($9,500 fixed scope) 2 weeks, senior analytics engineer, Senior Analytics Engineer ($4,200/mo) Full-time, 5–10+ years, Analytics Eng + BI Engineer ($7,800/mo) Pair build, 3–6 months. We confirm the right model after discovery so you can compare dedicated hiring, a focused sprint, or a small pod against the risk and timeline of your actual Analytics Engineer requirement.

We can support both models. If you already have strong product and engineering leadership, the engineer can plug into your process; if you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around trusted analytics models, semantic definitions, tests, documentation, and dashboards that leadership can use without second-guessing.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For Analytics Engineer hiring, that matters because the real risk is metric disputes, broken dashboards, unclear ownership, slow reporting requests, and business teams losing trust in data. You get a shorter path to qualified candidates and a trial structure focused on technical outcomes rather than resume volume.

Devlyn is a better fit when the Analytics Engineer work affects leadership reporting, product decisions, finance metrics, revenue operations, security, cost, or long-term maintainability. You get vetting, replacement support, delivery governance, IP protection, and continuity around outcomes like trusted analytics models, semantic definitions, tests, documentation, and dashboards that leadership can use without second-guessing.

Analytics Engineers are a strong fit when business teams need trusted metrics and self-serve decision systems. Common use cases include executive KPI reporting, product analytics foundations, revenue analytics, BI cleanup, dbt refactors, semantic-layer design, Looker or Power BI model cleanup, retention and cohort reporting, activation funnels, ARR and churn definitions, sales pipeline reporting, customer health scorecards, and AI-ready metric layers. If the need is narrower, we can help you decide whether one specialist, a full-time dedicated engineer, or a small delivery pod is the right model.