Job
Description
Position Overview: As a Senior Analytics Engineer, you will be responsible for the architecture, development and implementation of robust data engineering solutions to support our organization's data-driven initiatives to ensure data accuracy and enable data-driven decision-making across the organization. The ideal candidate will possess a minimum of 5 years of hands-on experience in data engineering on high-performing teams. Expertise in DataBricks, dbt, SQL, & Python is a critical requirement for this role. Key Responsibilities: 1. Data Pipeline Development: Design, develop, and maintain ETL (Extract, Transform, Load) pipelines primarily using Databricks, Dbt, and SQL to collect and process data from various sources into a centralized data warehouse. 2. Data Modeling: Create and maintain data models, data dictionaries, and documentation to support efficient data analysis and reporting. 3. Data Quality Assurance: Implement data validation and cleansing processes to ensure data accuracy, consistency, and reliability. 4. Data Governance: Ensure data security, compliance, and governance standards are met, and contribute to data governance initiatives. 5. Mentor and develop the analytics engineering team, and provide best practice guidance to broader analytics community. 6. Collaboration: Collaborate with cross-functional teams, including data scientists, business analysts, and stakeholders, to understand their data needs and deliver solutions. 7. Performance Optimization: Continuously monitor and optimize data pipelines and analytics processes for efficiency and scalability. 8. Adhoc Data Analysis and Reporting/Dashboard Development: Perform exploratory data analysis, develop data visualizations, and generate actionable insights to support business decision-making. 9. Stay Current: Stay up-to-date with emerging trends and technologies in data engineering and analytics, and make recommendations for their adoption. Qualifications: Bachelor's degree in Computer Science, Information Technology, or a related field. Minimum 5+ years of hands-on experience in data engineering Expertise in building data pipelines and ETL processes using Data Bricks, Dbt and Python Strong understanding of data warehousing concepts and methodologies. Experience with cloud platforms such as Azure, AWS or GCP Excellent communication and interpersonal skills Excellent problem-solving skills and attention to detail Strong communication and teamwork abilities. Knowledge of data security and compliance standards is a plus
