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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analytics Engineer - **Company:** Top Employers Institute - **Location:** Amsterdam, Netherlands (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Business Analytics Applications, Data Analysis, BigQuery, Software as a Service, Cloud Computing, Code Review, Continuous Integration, Customer Data Management, Information Engineering, Data Governance, Google Analytics, Python (Programming Language), Power BI, Salesforce.Com, SQL Databases, Tableau (Software), Web Platforms, Business Intelligence Development Studio, Snowflake, Salesforce Sales Cloud, Data Layers, Build Management, Git Flow, Qlikview, Pardot B2B Marketing Automation (Salesforce), Looker Analytics, Software Version Control, Databricks - **Published:** July 15, 2026 - **Apply:** https://www.adzuna.nl/details/5775572983 ## About the Role * Ensures accountability * Tech savvy * Strategic mindset * Plans and aligns * Manages complexity * Cultivates innovation * Drives engagement * Optimizes work processes * Balances stakeholders Qualification Criteria Must have * 6-9 years building modern data platforms end-to-end * Expert SQL and strong data modelling (dimensional/Kimball and metric/semantic modelling) * Production experience with dbt (or equivalent), including testing, CI/CD, and git workflows; experience with semantic-layer technologies (dbt Semantic Layer, MetricFlow, Cube, LookML etc) * Hands-on experience with a cloud warehouse/lakehouse (Databricks, Snowflake, BigQuery etc) * Experience with ELT/ingestion tools (Fivetran, Airbyte) and orchestration (Airflow, Dagster, dbt Cloud) * Proficiency in Python for data engineering * Strong understanding of data governance, security, and multi-tenant/row-level access control * Ability to work autonomously and set architecture and engineering standards * Clear communicator comfortable with non-technical stakeholders Nice to have * Embedded analytics and BI tooling experience (Power BI, Tableau, Qlik, Looker etc) * Familiarity with Salesforce data model and B2B/Human Resources data domains * Exposure to conversational analytics, RAG, or text-to-SQL patterns * Experience serving analytics to external customers in a multi-tenant SaaS context ## Description This role sits within the Analytics team, part of the broader Digital Team at Top Employers Institute. The Digital Team builds and supports the digital platforms that run our core business, Salesforce Sales Cloud, Experience Cloud (our client portal), Pardot, and CRM Analytics, underpinning processes across Marketing, Sales, Operations (Certifications) and Finance, and giving our clients access to surveys, analytical insights, and best practices. Within that, the Analytics team drives insight across multiple domains to enable data-informed decisions. For our clients, we build dashboards that turn survey data into deep insight on HR performance to help them improve their HR practices. Internally, we serve the Product team with portal-usage insights from Google Analytics to optimise the user experience, support Client Success with client-health analytics that strengthen retention, and give Marketing and Sales lead-to-cash analysis that sharpens prospecting and revenue generation. Today this breadth is delivered on a single-tool setup. We are now modernising it into a decoupled, cloud-native platform with a governed semantic layer at its core, one trusted set of metric definitions serving internal reporting, embedded analytics in our client-facing portal, and a new generation of conversational/agentic analytics. As the Senior Analytics Engineer, you are the foundational engineering hire for that journey: you will architect and build the platform from the ground up, not maintain someone else's. As the first dedicated platform engineer, your decisions set the standard for how the Analytics team models, governs, and serves data across all of these domains as it grows. Key Responsibilities * Design and build the data foundation. Stand up a cloud warehouse/lakehouse and migrate analytical workloads from the legenioacy single-tool setup. * Own transformation as code. Implement version-controlled, tested dbt pipelines (raw * staging * marts) with CI/CD, documentation, and code review. Include experience with semantic-layer technologies (dbt Semantic Layer, MetricFlow, Cube, LookML) in this item. * Implement a governed semantic layer. Define and serve metric definitions consistently to BI, embedded analytics, and AI consumers. * Ingestion and orchestration. Land data from Salesforce and other sources using managed ingestion (Fivetran/Airbyte or similar) and operate reliable orchestration. * Governance, security, and observability. Deliver cataloging, lineage, data-quality tests, observability, and row-level/tenant isolation for safe external exposure. * Enable product analytics. Provide a framework-agnostic data layer for embedded analytics and a foundation for conversational/agentic analytics. * Set engineering standards. Establish testing, version control, documentation, and review practices for a growing data function. * Partner with stakeholders. Translate analyst, data scientist, product, and business requirements into reusable, reliable data products. 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