Hire Remote Data Analysts & BI Developers

Hire Data Analysts Who Turn Data Into Decisions

Every business decision that isn’t data-driven is a guess. Data Analysts and BI Developers are the people who make that data accessible — who translate raw data into dashboards, reports, and ad hoc analyses that let product managers, marketers, executives, and operators make better decisions faster.

We match you with senior Data Analysts and BI Developers who’ve built analytics functions for SaaS companies, e-commerce platforms, and enterprise organizations. Analysts who are fluent in SQL, proficient in BI tooling (Looker, Tableau, Power BI), and skilled at translating business questions into analytical frameworks that produce actionable answers.

Start in days, not months. Pay 50% less than equivalent US-based data analytics talent.

What Our Data Analysts & BI Developers Deliver

Business Intelligence Dashboard Development

Tableau, Looker, Power BI, and Metabase dashboards that executives and operators actually use — built around the KPIs that matter, with self-service filtering that lets stakeholders answer follow-up questions without submitting another analytics request.

SQL Analytics & Ad Hoc Analysis

Complex SQL analysis across data warehouses (Snowflake, BigQuery, Redshift) — cohort analysis, funnel analysis, retention curves, revenue attribution, customer segmentation, and the ad hoc investigation that answers urgent business questions.

dbt Analytics Engineering

dbt model development, documentation, and semantic layer design — building the analytics engineering layer between raw data and BI tools that gives analysts stable, tested data models instead of fragile point-to-point SQL.

A/B Test Analysis & Experimentation

Statistical analysis of A/B tests: sample size calculation, significance testing, confidence intervals, and the interpretation frameworks that tell product managers whether an experiment’s results mean what they think they mean.

Self-Service Analytics Enablement

Building the data infrastructure that lets non-technical stakeholders answer their own data questions: Looker Explores, Tableau Server views, Power BI datasets, and the training and documentation that turns data into a self-service organizational capability.

Data Analyst & BI Developer Stack

BI Tools: Looker (LookML), Tableau, Power BI, Metabase, Mode, Redash, Superset

SQL: PostgreSQL, Snowflake, BigQuery, Redshift, DatabricksSQL, dbt

Languages: Python (pandas, matplotlib), R (for statistical analysis), JavaScript (embedded analytics)

Data Modeling: dbt, dimensional modeling, OBT (One Big Table) patterns, semantic layer design

Statistics: Cohort analysis, A/B testing, regression, time-series analysis, customer lifetime value modeling

Client Success Story: SaaS Company — Executive Dashboard Replaces 40 Hours/Week of Manual Reporting

A Series B SaaS company’s finance and revenue operations team was spending 40 hours per week producing manual Excel reports for the executive team — exports from Salesforce, Stripe, and their product database stitched together in spreadsheets that broke constantly as the business grew. Our Data Analyst built a Looker-based executive dashboard covering ARR, MRR, churn, expansion revenue, pipeline, CAC, and LTV — pulling from a clean dbt data model built on Snowflake. Dashboard data refreshed hourly. The 40 hours per week of manual reporting was eliminated. Executives stopped asking for data because they could answer their own questions. The revenue operations team used the saved time to build their first customer health scoring model.

Client Success Story: E-Commerce — Product Analytics Uncovers $4M Conversion Opportunity

A mid-market e-commerce company’s product team was making roadmap decisions based on intuition and HiPPO (Highest Paid Person’s Opinion) dynamics — no product analytics framework, no funnel visibility, no A/B testing rigor. Our Data Analyst built a product analytics function from scratch: Mixpanel event taxonomy, SQL funnel analysis in their Snowflake warehouse, a Tableau dashboard tracking conversion rates by traffic source, device type, and product category, and an A/B testing framework with proper statistical significance thresholds. The first proper funnel analysis identified that mobile checkout had a 23% lower conversion rate than desktop on the same SKUs. A focused mobile checkout redesign test, properly analyzed, showed 31% improvement. Annualized revenue impact from the mobile conversion improvement: $4M.

Why Companies Choose Our Data Analysts & BI Developers

• Business-question focused: They start with what decisions the data should inform, not with the data they have available
• SQL depth: They write complex analytical SQL efficiently — joins, window functions, CTEs, and the query patterns that make ad hoc analysis fast
• BI tool expertise: They build dashboards that stakeholders actually use — not dashboards that look impressive in demos but confuse users in practice
• 50% cost savings: Senior data analytics expertise at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Individual Data Analyst — One senior analyst embedded with your team for dashboard development, ad hoc analysis, and analytics function building.
• Analytics + Data Engineering Pod — Data Analyst focused on business analysis paired with a Data Engineer owning pipeline and warehouse infrastructure.
• Analytics Teams — Multiple analysts for large organizations with high analytics demand across product, marketing, finance, and operations.
• Contract-to-Hire — Evaluate an analyst’s SQL quality, business acumen, and dashboard design before committing long-term.

How To Vet Data Analysts & BI Developers

Our vetting identifies analysts who produce insights that drive decisions — not just data visualizations.

1. SQL assessment — Multi-table join query, window functions, and a complex aggregation problem. We evaluate query correctness, query optimization, and SQL style. Over 80% of applicants do not pass this stage.
2. Dashboard design review — Review a dashboard they’ve built. We evaluate layout clarity, metric definition appropriateness, filter design, and whether the dashboard answers the business questions it claims to address.
3. Business problem translation — Given a business problem (declining retention, high CAC, slow growth in a product area), how do they translate it into an analytical approach? What data do they need? What analysis would they run?
4. Stakeholder communication — How do they present analysis results to non-technical executives? How do they handle “but I thought the answer would be X”? Analytical communication is as important as analytical skill.

What to Look for When Hiring Data Analysts & BI Developers

Strong data analysts improve business decision quality — they don’t just produce charts.

What strong candidates demonstrate:

• They start by understanding the decision the analysis should inform — they don’t immediately start writing SQL
• They write SQL that’s readable and maintainable — not just queries that return the right answer
• They understand statistical significance — they won’t report a 5% conversion improvement as a win without knowing if it’s statistically meaningful
• They’ve built BI infrastructure that non-technical stakeholders actually use independently

Red flags to watch for:

• Excel-first analysts without warehouse SQL fluency — can’t work at scale or in modern data stacks
• Dashboard builders who don’t understand the business questions their dashboards should answer
• No understanding of A/B testing statistics — treats any positive result as a win
• Poor data storytelling: can find an interesting number but can’t explain why it matters or what to do about it

Interview questions that reveal real depth:

• “Walk me through a piece of analysis that changed a product or business decision. What was the question, what was your approach, and what happened as a result?”
• “How do you handle a situation where an executive doesn’t like the analysis results and asks you to ’re-run it differently’?”
• “Write a SQL query that computes 30-day, 60-day, and 90-day retention rates for a cohort of users who signed up in January.”

Related Services

• Data Engineers — Data pipeline engineers who build the reliable data infrastructure analysts depend on.
• Data Scientists — ML engineers who build predictive models on top of the analytics foundation data analysts establish.
• Quantitative Analysts — Statistical and financial modeling specialists for fintech, trading, and risk management applications.
• Product Managers — Product managers who rely on data analysts to measure product performance and validate experiment results.

Want to Hire Remote Data Analysts & BI Developers?

We source, vet, and place senior Data Analysts and BI Developers who turn raw data into the insights and dashboards that drive better business decisions — analysts who are SQL-fluent, BI-tool expert, and focused on business outcomes rather than just data visualization. Whether you need one analyst or a complete analytics team, we make it fast, affordable, and low-risk.

Get matched with Data Analysts →

Ready to hire Data Analysts who turn data into business decisions? Contact us today and we’ll introduce you to senior data analysts within 48 hours.