At a Glance
- Tasks: Own and execute the vision for our data transformation layer using dbt and SQL.
- Company: Join a 100% remote team at a leading analytics firm.
- Benefits: Home-office setup, monthly stipend, stock options, and health benefits from day one.
- Other info: Collaborate with diverse teams and enjoy a dynamic, fast-paced work environment.
- Why this job: Make a real impact by building scalable data models that drive business decisions.
- Qualifications: 4+ years in analytics engineering, expert-level SQL, and experience with cloud environments.
- We are seeking an Analytics Engineer to own and execute the vision for our data transformation layer.
- You will be at the heart of our data platform, which processes hundreds of millions of events daily from a wide array of sources, including transactional databases, API logs, CRMs, payment systems, and marketing platforms
- You will join our 100% remote team and work closely with Data Engineers (who manage data ingestion) and Data Scientists and Business Users (who consume your data models).
- Your primary responsibility will be to use dbt and Trino on our GCP-based, open-source data infrastructure to build robust, scalable data models.
- These models are critical for stakeholders across the company—from finance and operations to the executive team—and are delivered via BI tools, reports, and reverse ETL systems
- Own the Transformation Layer: Design, build, and maintain scalable data models using dbt and SQL to support diverse business needs, from monthly financial reporting to near-real-time operational metrics
- Set Technical Standards: Establish and enforce best practices for data modeling, development, testing, and monitoring to ensure data quality, integrity (up to cent-level precision), and discoverability
- Enable Stakeholders: Collaborate directly with finance, operations, customer success, and marketing teams to understand their requirements and deliver reliable data products
- Integrate and Deliver: Create repeatable patterns for integrating our data models with BI tools and reverse ETL processes, enabling consistent metric reporting across the business
- Ensure Quality: Champion high standards for development, including robust change management, source control, code reviews, and data monitoring as our products and data evolve
Benefits
- New Hire Home-Office Setup: One-time USD $500
- Monthly Stipend: USD $150 per month via a Brex Card
- Work with awesome people, clients and partners from around the world
- Stock Options
- Medical, Dental, Vision
- Supplemental health care
- Health benefits from day 1 (internationally, this includes a stipend value to offset medical costs)
- Comfortable working with ambiguity and collaborating with stakeholders to define requirements; able to take ownership with minimal oversight in a fast-paced environment
- Familiarity with cloud environments (GCP or AWS)
- Expert-level SQL and DBT skills for complex queries and data transformations
- Proven track record of owning data products end-to-end, applying analytics and data engineering best practices to ensure data quality, scalability, and robust data models
- Experience owning CI/CD workflows and establishing team-wide standards for version control and code review (e.g., Git)
- Experience proactively identifying and implementing improvements to data warehouse performance and ETL efficiency
- Proficiency with Semantic Layer modelling (e.g. Cube, dbt Semantic Layer)
- Hands-on experience with query optimization across OLTP and OLAP systems (e.g., Postgres, Iceberg)
- Proficiency in Python for transformations that extend beyond SQL4+ years of experience in analytics engineering or data engineering with a strong focus on the “T” (transformation) in ELT
- Experience with data ingestion tools (e.g., Airbyte) and orchestration tools (e.g., Airflow)
- Domain experience for brokerage operations or passion for financial markets and modeling financial datasets
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Senior Analytics Engineer employer: Alpaca
Alpaca is an exceptional employer that fosters a collaborative and innovative work culture, particularly for the Acquisition Engineering Lead role based in the UK. Employees benefit from competitive salaries, comprehensive health benefits, and support for home-office setups, all while having ample opportunities for professional growth and development within a dynamic team focused on enhancing user experiences.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Alpaca!
✨Show Off Your Projects
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Alpaca.
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When you find a suitable opening like Senior Analytics Engineer at Alpaca, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Alpaca, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Alpaca. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Alpaca
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Alpaca!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
