Data Analytics Engineer

Are you passionate about data analytics and eager to work on large-scale projects? Fingerprint is looking for an Analytics Engineer to join our Identification Team. You will play a crucial role in providing high-quality tools for deep data analytics over our Identification API Product, which processes approximately 1 billion requests per month.

Responsibilities:

• Develop tools for deep data analytics on the Identification API Product.
• Provide insights about algorithm accuracy, design experiments, detect anomalies, and analyze trends in time-series data.
• Foster an engineering-focused, data-driven culture within the Fingerprint team by sharing tools and knowledge on effective data analytics approaches.
• Apply software engineering best practices to deliver clean, transformed data ready for analysis.
• Strengthen the team behind the industry-leading device identification API through applied data analytics skills.
• This role includes participation in a shared on-call rotation. The schedule will be communicated in advance, and we strive to balance coverage equitably while minimizing off-hours disruptions.

Qualifications:

• Proficiency in English for clear communication within an international remote team.
• BS/MS in Computer Science or a related field, or equivalent work experience.
• 2+ years of experience in Analytics Engineering, Data Engineering, Data Analytics, or Data Science.
• Excellent SQL skills.
• Practical experience with analytical storage solutions such as Clickhouse, Snowflake, BigQuery, Redshift, Databricks, etc.
• Familiarity with data transformation frameworks and approaches (e.g., dbt, materialized views, data pipeline workflow tools).
• General engineering skills: git, IDE, shell.
• Experience with data visualization tools like Apache Superset, Tableau, Metabase, Looker, etc.
• Strong foundation in statistics for designing metrics and experiments.
• Ability to conduct Exploratory Data Analysis (EDA) for investigating ad-hoc questions and identifying anomalies.

Nice to Haves:

• Proficiency with the Python data analytics stack (Numpy, pandas, Jupyter, etc.).
• Machine Learning skills, particularly for quality estimation of ML algorithms.

Technologies:

• Our stack: Clickhouse, dbt, Apache Superset (Preset), Prefect
• Relevant technologies: Google BigQuery, Amazon Redshift, Snowflake, Apache Druid, Vertica, DataBricks, Apache Airflow, Dataform, Talend, Fivetran, Stitch, Luigi, Dagster, Tableau, Power BI, Looker, Metabase, Redash