Analytics Engineer

Job Summary:
An Analytics Engineer is a data professional who transforms raw data into clean, reliable, and analysis-ready datasets that drive business insights. This role bridges the gap between data engineering and data analysis by applying software engineering principles to data workflows, building robust data models, ensuring data quality, and collaborating closely with cross-functional teams to support data-driven decision-making.

Key Responsibilities:

Data Modeling & Transformation

• Design, build, and maintain scalable data models, tables, and views that support analytical use cases.
• Transform raw data into structured, high-quality datasets using SQL and data transformation tools (e.g., dbt).

Data Pipeline Development

• Build and optimize data pipelines that load, transform, and ensure timely delivery of analytics-ready data.
• Implement automation for data workflows and schedule recurring processes (e.g., with Airflow or cloud schedulers).

Quality Assurance & Testing

• Establish and enforce data quality standards through automated tests, validations, and monitoring.
• Resolve data discrepancies by identifying root causes and implementing fixes in code or pipeline logic.

Documentation & Governance

• Create and maintain clear documentation including data dictionaries, model definitions, transformation logic, and lineage.
• Apply version control practices and software engineering best practices (Git, CI/CD) to analytics code.

Collaboration & Stakeholder Engagement

• Work closely with data analysts, business stakeholders, and product teams to gather requirements and translate business needs into technical solutions.
• Provide training and support to end-users on how to access and interpret data assets.

Visualization Support

• Enable data analysts and BI teams by preparing analytics-ready datasets used in dashboards and reports.
• Contribute to the development of metrics and reporting standards.

Technical Skills & Tools:

• SQL: Advanced proficiency for querying and transforming data.
• dbt (or similar transformation frameworks) for modular data transformation and testing.
• Familiarity with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
• Experience with ETL/ELT processes and orchestration tools (e.g., Airflow, Prefect).
• Knowledge of basic scripting languages (e.g., Python) for automation tasks.
• Version control (Git), testing frameworks, and documentation best practices.

Qualifications:

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field (or equivalent experience).
• Proven experience building analytics systems, data models, and pipelines in a collaborative environment.
• Strong analytical and problem-solving skills with attention to data accuracy and consistency.

Desired Soft Skills :

• Excellent communication and stakeholder management.
• Ability to break down complex business requirements into technical solutions.
• Team collaboration across engineering, analytics, and business teams.