Lead Microsoft Analytics Engineer
il y a 3 semaines
As a pivotal member of the data team, you will engage in cross-departmental collaboration to drive data-centric initiatives that benefit Fabulous and its various business sectors, including Product Growth, User Acquisition, and Finance.
The team is responsible for three primary data domains:
Data Project Management:
We prioritize and manage requests based on business requirements, refining specifications and acceptance criteria in collaboration with stakeholders. Our approach includes planning and executing projects while maintaining proactive communication to ensure transparency and a robust feedback loop.
Applied Analytics & Data Science:
Our focus is on data exploration and establishing agile analytics methodologies.
Analytics Engineering:
We specialize in data modeling and transformations to construct, sustain, and enhance our analytics pipelines. A well-structured architecture that mitigates technical debt is essential in this area.
All team members are expected to demonstrate proficiency in at least two of the aforementioned domains to contribute effectively and independently.
We adopt an agile methodology, breaking larger projects into manageable iterations that typically do not exceed three weeks, ensuring meaningful impact.
Our modern cloud-based data stack includes tools such as Fivetran, BigQuery, dbt, Amplitude, Metabase, and Looker Studio. We are looking to strengthen our team with a well-rounded Senior Analytics Engineer who can seamlessly integrate into our agile environment and deliver value promptly.
This role is crucial for the ongoing success of the data science team:
You will engage in data modeling and analytics engineering tasks aimed at enhancing, enriching, and maintaining our data models and analytics pipelines in close collaboration with the head of Data & Analytics.
Your responsibilities will include:
- Contributing effectively to our codebase by building, testing, reviewing, and maintaining robust analytics pipelines utilizing SQL and dbt.
Qualifications:
- A minimum of 4 years of experience in Data or Analytics Engineering.
- Exceptional hands-on skills in Data Modeling.
- Strong SQL proficiency (experience with dbt is advantageous).
- Solid engineering capabilities (testing, clean coding, peer reviewing, CI/CD, git workflows, agile methodologies).
- Prior experience with modern data stack tools and cloud-based data warehouses (BigQuery, Snowflake, Redshift).
- Excellent written and verbal communication skills in English.
- Comfortable working in a remote environment.
- Familiarity with tools in our data stack (Amplitude, dbt, BigQuery, Metabase).
- Hands-on data analysis experience.
- Experience managing modern dbt data pipelines.
- Background in an agile startup setting.
Our science-driven approach empowers individuals to lead fulfilling lives. Our vision has been nurtured within an incubation lab for startups at a prestigious university.
While we operate remotely, it is common to develop a deeper connection with your colleagues than with those you have previously shared a physical workspace.
Supported by prominent incubators and venture partners.
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