We are seeking a Senior Analytics Engineer to join our team. As a key member of our data organization, you will be responsible for transforming raw data into a strategic asset by designing high-performance data models that power our financial reporting, product forecasting, and GTM strategy.
\nYour 12-Month Journey
\nDuring the first 3 months, you will learn about our existing stack (GCP, BigQuery, Airbyte, dbt), core business data models, and understand the current pain points in our data flow. You will deliver and optimize your first high-priority models for product usage and financial reporting. You will partner with the Data Engineer to align on the new infrastructure roadmap.
\nWithin 6 months, you will implement a robust semantic layer to standardize KPIs across the company and enable AI-readiness and advanced natural language querying.
\nAfter 1 year, you will fully own the company's data modeling architecture, ensuring it is prepared for AI and machine learning applications. You will act as a strategic advisor to department heads, using data to help shape the company's long-term growth and forecasting strategies.
\nWhat You'll Be Doing
\nStrategic Data Product Ownership: Manage the end-to-end lifecycle of our internal data products. You will partner with stakeholders to translate complex business questions into technical requirements, selecting the right tools to ensure our reporting is scalable, accessible, and high-impact.
\nAdvanced Analytics Engineering: Design, build, and maintain our core data models using dbt Labs. You will own the logic for mission-critical datasets, including financial reporting, churn forecasting, and reverse-ETL flows that sync warehouse data back into our business tools (e.g., Planhat, HubSpot).
\nData Governance & Semantic Layering: Act as the guardian of 'The Truth.' You will implement data governance standards and build our semantic layer to ensure metrics are consistent across the company.
\nData Democratization & Enablement: In collaboration with RevOps, you will design and deliver training programs and documentation. Your goal is to empower users across Finance, Product, and GTM to independently navigate data products and derive their own insights.
\nCollaboration: You will be the central hub of our data organization. You will work daily with the Data Engineer to align on the roadmap, while frequently consulting with Finance, GTM, and Product leaders to ensure our data products solve their most pressing problems.
\nWhat You Bring
\nSolid experience in Analytics Engineering, Data Analysis, or Data Engineering, with a track record of independently delivering data products that enable reporting, decision-making, and CDP use cases.
\nYou are an expert in SQL and understand how to write performant, modular code. Familiarity with Python and Git for optimizing and versioning data transformations is a significant advantage.
\nDeep, hands-on experience with dbt and BigQuery is a must. You should also be comfortable navigating ELT tools like Airbyte or Fivetran.
\nCommercially savvy: you understand the business. You can spot opportunities where data can improve ARR, reduce churn, or optimize spend.
\nYou thrive in fast-paced environments and are comfortable creating structure out of the uncertainty of a scaling company.
\nStrong project management and stakeholder management skills. You are a 'bilingual' communicator who can discuss warehouse schemas with an engineer and ARR growth with a CFO.
\nFluency in English, both written and spoken, at a minimum C1 level
\nWhat We Offer
\nFlexibility to work from home in the Netherlands and from our beautiful canal-side office in Amsterdam
\nA chance to be part of and shape one of the most ambitious scale-ups in Europe
\nWork in a diverse and multicultural team
\n€1,500 annual training budget plus internal training
\nPension plan, travel reimbursement, and wellness perks
\n28 paid holiday days + 2 additional days to relax in 2026
\nWork from anywhere for 4 weeks/year
\nAn inclusive and international work environment with a whole lot of fun thrown in!
\nApple MacBook and tools
\n€200 Home Office budget
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\nThis role exists to bridge the gap between "getting data" and "engineering data," moving us from manual syncs to a fully automated ecosystem. By building custom pipelines and implementing a robust orchestration layer, you will directly enable our Operations teams and leadership to transition from basic reporting to sophisticated, AI-ready data products.
\nYour primary focus will be on Infrastructure-as-Code, orchestration, and building a resilient "plumbing" system that serves as the backbone for our entire Product and GTM strategy.
\nYour 12-Month Journey During the first 3 months: you will learn about our existing stack (GCP, BigQuery, Airbyte, dbt) and understand the current pain points in our data flow. You will identify and execute "low-hanging fruit" improvements to our product usage analytics, providing immediate value to the Product and GTM teams. You’ll begin designing the blueprint for our custom data pipelines and the migration strategy for moving our infrastructure into Terraform.
\nWithin 6 months: You will have deployed our new orchestration layer (e.g., Airflow or Dagster) and successfully transitioned our first set of custom pipelines to production. Collaborating with the Analytics Engineer, you will enable a unified view of our customer journey by successfully merging product usage data with CRM and billing data. At this point, a significant portion of our data infrastructure will be defined as code, reducing manual overhead and increasing deployment reliability.
\nAfter 1 year: you will take full strategic ownership of the data platform and its long-term architecture. You will act as the go-to technical expert for the leadership team, advising on the scalability of new data-driven features. You will lay the groundwork for AI and Machine Learning initiatives by ensuring our data warehouse has the right quality controls, governance, and low-latency access patterns in place.
\nWhat You’ll Be Doing Architect Scalable Infrastructure-as-Code: Take our existing foundations to the next level by migrating all GCP and BigQuery resources into Terraform. You will establish automated CI/CD patterns to ensure our entire data environment is reproducible, version-controlled, and enterprise-ready.
\nDeploy State-of-the-Art Pipelines: Design, deploy, and operate high-quality production ELT pipelines. You will implement a modern orchestration layer (e.g., Airflow or Dagster) to build custom Python-based integrations while maintaining and optimizing our existing syncs.
\nChampion Data Quality & Performance: Act as the guardian of our data platform. You will implement rigorous testing and monitoring protocols to ensure data is accurate and timely. You will proactively identify BigQuery bottlenecks, optimizing query performance and resource utilization.
\nTechnical Roadmap & Ownership: scope and architect end-to-end data flows from production source to warehouse. Manage your own technical backlog, prioritizing infrastructure stability over technical debt. You will ensure platform security and SOC2 compliance through PII masking, data contracts, and robust access controls.
\nCollaboration: You will work in a tight loop with the Analytics Engineer to turn raw data into actionable products. You will partner daily with DataOps and RevOps to understand business requirements, with occasional strategic syncs with DevOps and R&D to align on production schema changes and global infrastructure standards.
\nWhat You Bring Solid experience in Data Engineering, with a track record of building and evolving data ingestion infrastructure in cloud environments. The Modern Data Stack: Familiarity with dbt and Airbyte/Fivetran. You understand how these tools fit into a broader ecosystem. Expertise in BigQuery (partitioning, clustering, IAM) and the broader GCP ecosystem; Infrastructure-as-Code (Terraform). Hands-on experience with Airflow, Dagster, or similar orchestration tools. You know how to design DAGs that are resilient and easy to debug. DevOps practices in the data context: familiarity with CI/CD best practices as they apply to data (data testing, automated deployments). Programming: Expert-level Python and advanced SQL. You are comfortable writing clean, testable, and modular code. Comfortable in a fast-paced environment Project management skills: capable of managing stakeholders, explaining complicated technical trade-offs to non-technical users, and taking care of own project scoping and backlog management. Fluency in English, both written and spoken, at a minimum C1 level
\nWhat We Offer Flexibility to work from home in the Netherlands and from our beautiful canal-side office in Amsterdam A chance to be part of and shape one of the most ambitious scale-ups in Europe Work in a diverse and multicultural team €1,500 annual training budget plus internal training Pension plan, travel reimbursement, and wellness perks 28 paid holiday days + 2 additional days to relax in 2026 Work from anywhere for 4 weeks/year An inclusive and international work environment with a whole lot of fun thrown in! Apple MacBook and tools €200 Home Office budget
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\nYou'll be part of our new Dynamic Pricing & Revenue Management team, working alongside a Data Scientist and a Data Analyst. Together, you will work towards one core goal: helping hosts improve occupancy and earnings through a smart, dynamic, and data-driven pricing strategy.
\nOur Tech Stack
\nYour role in this journey
\nAs a Data Ops Engineer – Revenue Management, you'll be the engineering backbone that enables our Data Scientists to move from experimentation to production. You bridge the gap between data science models and reliable, scalable production systems.
\nResponsibilities
\nBenefits
\nExperience
\nHow to apply
\nIf you're excited about this opportunity, please submit your application on our careers page!
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\nJob Description:
\nBefore 1965, it was extremely difficult and time-consuming to analyze complicated signals, like radio or images. You could solve it, but you had to throw a ton of compute at it. That all changed with the invention of the Fast Fourier transform, which could efficiently break that signal down into the frequencies that are a part of it.
\nThe Risk Onboarding team is working on efficiently reviewing customers’ applications without compromising on quality. We are the front line of defense for preventing money laundering and financial crimes, building systems to verify that someone is who they say they are and that we are allowed to do business with them.
\nAbout Us:
\nAt Mercury, we craft an exceptional banking experience for startups. Our team is focused on ensuring our products create a safe environment that meets the needs of our customers, administrators, and regulators.
\nJob Responsibilities:
\nAs part of this role, you will:
\nIdeal Candidate:
\nThe ideal candidate for the role has:
\nTotal Rewards Package:
\nThe total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
\nSalary Range:
\nOur target new hire base salary ranges for this role are the following:
\nDiversity & Belonging:
\nMercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic.
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