Analytics Engineer

As an Analytics Engineer, you will be responsible for transforming raw data from our applications into structured datasets for large-scale analysis and machine learning models. You will work closely with our development, data science, and business intelligence teams to ensure data integrity, quality, and accessibility.

• Data Modeling: Research and work with business stakeholders to develop our data warehouse model.
• Data Transformation: Clean, transform, and enrich data to create high-quality datasets suitable for analysis and machine learning.
• Collaboration: Work closely with product teams, software developers, data scientists, and analysts to understand data needs and deliver innovative solutions.
• Data Management: Ensure data accuracy, consistency, and reliability across all datasets.
• Optimization: Optimize data processes for performance and scalability.
• Documentation: Maintain comprehensive documentation of transformation logic and lineage.

• Educational Background: Bachelor’s degree in computer science, Data Engineering, or a related field.
• Experience: 3+ years of experience as an Analytics Engineer or in a similar role.
• Strong Communication skills: Ability to work with technical and non-technical audiences to translate business requirements into data models.
• Technical Proficiency:
  • Data Warehousing: Knowledge of data warehousing concepts and solutions (e.g., Redshift, Snowflake).
  • Data modeling: experience in modern data modeling practices, ideally dimensional modeling.
  • Programming Languages: Proficiency in SQL and a familiarity with Python.
  • Data Processing: Experience with ETL tools and frameworks (e.g., Apache Airflow, Luigi, DBT).
  • Database Management: Strong knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).
  • Version Control: Proficient with version control systems (e.g., Git).
  • Machine Learning: Understanding of machine learning concepts and experience working with data for ML model training.
  • AI: Familiarity and enthusiasm for bleeding-edge analytical enablement using tools such as Large Language Models and Prompt Engineering.