Data Analyst

As a Data Analyst with an analytics engineering focus, you will be the bridge between our raw data and our business stakeholders. You won't just build dashboards; you will own the entire analytics workflow from modeling and transformation to visualization and deep-dive analysis.

Using your expertise in SQL, Python, and modern BI tools, you will be responsible for creating and maintaining the trusted datasets that the entire company will rely on for decision-making. You will work closely with our Senior Data Engineer to leverage the data platform, ensuring that the data models you build are robust, reliable, and directly answer the most critical business questions.

Key Responsibilities :

● Data Modeling & Transformation: Use dbt to build, maintain, and document robust, reusable data models. You will own the "T" (Transform) in our ELT pipeline, turning raw data from our data lake into clean, trusted, and analysis-ready datasets.

● Business Intelligence & Dashboarding: Develop, deploy, and maintain insightful and intuitive dashboards using BI tools like Power BI, Tableau, or Metabase. You will be responsible for creating a single source of truth for key business metrics.

● Deep-Dive Analysis: Go beyond dashboards to answer complex business questions. Use your analytical skills in SQL and Python to conduct exploratory analysis, identify trends, and provide actionable recommendations to product, marketing, and leadership teams.

● Stakeholder Collaboration: Partner with business stakeholders to gather requirements, define key performance indicators (KPIs), and ensure your analytical outputs are aligned with their strategic goals.

● Data Quality & Documentation: Work with the Data Engineering team to define data quality tests within the transformation layer. Meticulously document your data models and metrics to foster a culture of data literacy.

Required Skills & Experience (Must-Haves) :

● 3+ years of experience in a data analyst, business intelligence, or analytics engineering role.

● Expert-level proficiency in SQL is absolutely essential. You should be comfortable with complex joins, window functions, and query optimization.

● Proven experience with a modern BI platform like Power BI, Tableau, Looker, or Metabase, from data source connection to final dashboard design.

● Hands-on experience with dbt for data modeling and transformation. You should understand dbt's core concepts and workflows.

● Proficiency in Python for data analysis and automation, specifically with libraries like Pandas, NumPy, and Matplotlib.

● Strong analytical and problem-solving skills, with a demonstrated ability to translate business questions into analytical work and analytical work into business insights.

● Excellent communication skills, with the ability to present complex data stories to a non-technical audience.

Preferred Skills & Experience (Nice-to-Haves) :

● Experience querying data in a cloud data warehouse or serverless query engine (e.g., AWS Athena, Google BigQuery, Azure Synapse, Snowflake).

● Familiarity with version control using Git.

● Experience working directly with data from NoSQL databases like MongoDB.

● A solid understanding of data engineering concepts (e.g., data lakes, ETL vs. ELT, data ingestion).

● Experience conducting statistical analysis (e.g., A/B testing, regression analysis).