Senior Data Engineer

Type: Full-time

Location: Remote (CET time zone overlap required)

Reports to: Head of Engineering

About the role

As the first data hire, you will build the data foundation the business depends on: reliable pipelines, a trustworthy warehouse, and high-volume first-party event data transformed into clean, performant, query-ready datasets.

On top of that foundation, you will help turn data into insight and insight into product intelligence, working with a genuine data-science awareness even if your center of gravity is engineering.

This is a hands-on building role. You will implement the pipelines, the models, and the datasets yourself, not write specs for someone else to build.

We are looking for a strong data engineer with enough data-science judgment to reason about metrics, experiments, and where machine learning or LLMs genuinely add value.

Because this will be a solo data function initially, self-sufficiency across the data stack is essential.

If you want to define what data means at a company and build the systems, models, and insights that shape its growth, this is the role.

What you will do

Data infrastructure (the core of the role)

● Design, build, and maintain the data infrastructure everything depends on, including ingestion pipelines, the data warehouse, and the transformation layer.

● Turn large volumes of raw, real-world event data into a trustworthy, query-ready foundation, owning performance, modeling, and data quality as volume grows.

● Integrate data from product databases, third-party services, and event streams at production scale into a single, trustworthy source of truth.

● Own data modeling and metric definitions so numbers mean the same thing across every team.

● Establish data quality checks, monitoring, and documentation that hold up as data volume and headcount grow.

Analytics and insights

● Turn raw data into insights that inform product,



growth, and operating decisions.

● Build and maintain dashboards and self-serve reporting so leadership and functional teams can answer their own questions.

● Define and track the core metrics that matter to the business, and surface trends before they become problems.

● Run analyses such as cohort and retention studies, funnel analysis, and A/B tests, and translate the findings into clear recommendations.

Data-powered product features

● Partner closely with founders and engineers to turn data into product features that customers use directly, such as recommendations, scoring, predictions, personalization, or in-product analytics.

● Build the models and datasets that power these features, and partner with engineering to get them shipped and kept healthy over time.

● Explore where machine learning and LLM-based approaches can create a genuine product edge and build them where they earn their place rather than for their own sake.

● Help shape the product roadmap by identifying where data can create real user value, not just internal reporting.

Cross-functional partnership

● Work closely with engineering, founders, and leadership to understand which decisions need data behind them.

● Communicate findings clearly to both technical and non-technical audiences and make the recommendation as well as the chart.

● Serve as the first point of contact for anything data related.

What we are looking for

Must have

● Several years as a data engineer,



with a genuine track record of building and owning production pipelines, warehouses, and transformation layers end to end.

● Strong SQL. It is the primary tool for working with our data.

● Proven ability to make large, production-scale datasets performant, and to model raw event data into something clean and query-ready.

● Solid data modeling judgment, and the discipline to keep metric definitions consistent and trustworthy.

● Python for data work (pandas and the surrounding ecosystem).

● Enough data-science grounding to be dangerous: comfortable with basic statistics and experimentation, and sound judgment about what is a real signal and when a model beats a simpler approach. You do not need a deep modeling background, but you need to reason well about the science.

● The ability to work independently, set your own priorities, and make progress without a manager spelling out the work.

● Clear written and verbal communication, including the judgment to frame an insight for a non-technical audience.

● Applied machine learning experience, and a sense of when it genuinely beats a simpler approach.

● NLP or LLM experience, and a sense of where it does and does not belong in a product.

● A cloud data warehouse (Snowflake, BigQuery, ClickHouse, or Redshift).

● Familiarity with dbt and an orchestration tool such as Airflow, Dagster, or Prefect.

● Experience with a modern BI tool such as Looker, Tableau, Power BI, or Metabase.

Nice to have

● A stronger data-science or modeling background than the baseline above.

● Experience as an early or first data hire at a startup.

● Domain experience in ad attribution, marketing or performance analytics, or e-commerce.

What we offer:

- Salary range between 100-110k annually
- Other benefits to be confirmed during the hiring process
- Fully remote setting

📌 Senior Data Engineer (España)
🏢 Carbon
📍 España