Analytics Engineer

BlueThrone • Capon Bridge, WV

48d

About The Position

Analytics Engineer
If you enjoy turning messy questions into clean numbers - and clean numbers into real decisions - keep reading
About BlueThrone
BlueThrone acquires and scales profitable mobile apps.
We don’t build from zero. We buy apps with strong potential and turn them into category leaders through better product decisions, smarter monetisation, and disciplined user acquisition.
Today, our portfolio generates tens of millions of downloads and serves millions of monthly active users across multiple verticals. We’re profitable, independent, and growing through disciplined bets - not hype.
Our teams are small and senior. The same people building the product are also discussing the numbers and making decisions.
Data sits at the center of everything we do - from retention analysis and pricing experiments to UA scaling and cohort profitability.
Your mission
As our Analytics Engineer, you’ll help define how we measure the business.
You’ll work closely with product, monetisation, UA, and finance leads to turn ambiguous questions into clear models and reliable metrics. Your job is to make sure our mobile KPIs are accurate, explainable, and most importantly trusted.
When leadership asks:
Why did Week-2 retention drop?
Is this cohort actually profitable?
Can we safely scale this campaign?
Did the pricing experiment improve LTV?
You make sure the answer comes from clean modeling, not "gut feel." This role is a 50/50 split between building elite data infrastructure (dbt + BigQuery) and digging into the numbers yourself when something looks "interesting."

Requirements

• 5+ years of hands-on analytics engineering or a senior analyst role with heavy SQL and modelling work.
• Strong SQL fluency and real experience with a cloud data warehouse (BigQuery preferred; Snowflake or Redshift also fine). You understand partitioning, clustering, and how to write cost-effective queries at scale.
• 2+ years of production dbt experience: you've built and maintained dimensional models, written incremental models, authored macros, and set up tests that catch real problems.
• Track record of building dashboards for non-technical stakeholders - people who judge your work by whether it helps them make better decisions, not by how clever the SQL is.
• Genuine curiosity about the data you work with. You don't just build the pipeline and move on - you explore, question, and proactively investigate when something looks off. We need someone who treats analysis as a core part of the job, not a favor to stakeholders.
• Ability to ramp up fast on unfamiliar business domains. You ask the right questions and aren't afraid to say "I don't understand this yet."
• Clear, concise communicator. You document your models, explain trade-offs to non-technical people, and proactively surface blockers.

Nice To Haves

• Experience in gaming, mobile apps, or any high-volume event-driven data environment.
• Familiarity with Metabase (or a willingness to pick it up in week one - it's straightforward).
• Experience with event tracking design (e.g., working with product/engineering on taxonomy and instrumentation).
• Familiarity with mobile attribution platforms (Adjust, AppsFlyer, or similar).
• Willingness to experiment with LLM for data work automation

Responsibilities

• Stakeholder partnership: Sit with product, finance, UA, and monetisation leads to translate fuzzy business questions into precise analytical requirements.
• Data modelling: Design and maintain the dbt project on BigQuery - dimensional models (fact/dim), incremental loads, reusable macros, thorough tests, and documentation that actually gets read.
• Dashboards & self-serve: Build and iterate on Metabase dashboards that give non-technical teams the confidence to make decisions without pinging you for every question.
• Data analysis & deep dives: Go beyond building pipelines - dig into the data, investigate anomalies, run ad-hoc analyses, and surface insights that stakeholders didn't know to ask for. This role is not just about infrastructure; it's about understanding what the numbers mean and why they matter.
• Data quality & reliability: Monitor production dbt runs, triage failures, trace issues from the dashboard layer all the way back to raw sources, and communicate impact and resolution to stakeholders.
• Mobile metrics expertise: Be the team's go-to on core mobile KPIs - DAU/MAU, LTV, retention cohorts, ARPU, ROAS - and ensure the models behind them are accurate and trustworthy.
• Process improvement: Identify gaps in our data stack or workflows and propose practical solutions. We value people who make the whole team better.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed