Data Analytics Engineer

As an Data Analytics Engineer, you play a critical role in building the company’s data transformation layer, bridging the gap between raw data ingestion and end-user consumption. Your primary mission is to develop clean, well-tested, well-documented, and high-performing analytical models that enable business stakeholders to answer their own questions with confidence.

Key Responsibilities:

• Data Modeling & Standards: Design and develop core data models that improve analytical efficiency and deliver fast, reliable insights, while defining and implementing data modeling standards and best practices using tools such as dbt.
• Data Operations Optimization: Improve data team productivity by optimizing operational tasks, automating workflows, and streamlining data processes to enhance efficiency, reliability, and scalability.
• Data Quality: Lead data quality initiatives and implement quality controls to ensure security, compliance, and high data quality standards are consistently met across the data platform.
• Business Collaboration & Reporting: Collaborate with Data Owners, Data Scientists and business stakeholders to translate requirements into effective data models, develop and maintain key dashboards and reports that track business performance and support strategic decision-making.

Required Skills & Qualifications:

• 4+ years of experience as an Analytics Engineer, Data Engineer, Data Analyst or a closely related role.
• Expertise in SQL: Ability to write complex, performant queries and understand advanced SQL concepts (window functions, CTEs, query optimization).
• Data Modeling: Deep understanding of dimensional modeling (Star Schema, Snowflake Schema), slowly changing dimensions (SCDs), and fact/dimension design.
• Tooling: Experience with modern data stack tools (e.g., dbt, BigQuery/Redshift, Airflow/Prefect, Git).
• Programming: Proficiency in Python for data manipulation, and workflow scripting.
• Engineering Mindset: Experience with version control (Git), and code review processes.
• Communication: Proven ability to translate complex technical requirements into business terms and effectively document data workflows.
• Education: Bachelor’s degree in Computer Science, Data Analytics, Information Systems, or a related quantitative field (or equivalent practical experience).

Preferred Qualifications

• Experience with cloud-based data warehousing and Lakehouse architectures (e.g., Databricks, BigQuery, Snowflake).
• Familiarity with data governance practices (data lineage, access control, PII management).
• Experience in a fast-paced, agile environment using Scrum or Kanban methodologies.
• Self-starter mindset, capable of independently driving projects from concept to execution with minimal supervision.