Senior Analytics Engineer
2 weeks ago
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:
- 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.
- Data Modeling: Create and maintain data models, data dictionaries, and documentation to support efficient data analysis and reporting.
- Data Quality Assurance: Implement data validation and cleansing processes to ensure data accuracy, consistency, and reliability.
- Data Governance: Ensure data security, compliance, and governance standards are met, and contribute to data governance initiatives.
- Mentor and develop the analytics engineering team, and provide best practice guidance to broader analytics community.
- Collaboration: Collaborate with cross-functional teams, including data scientists, business analysts, and stakeholders, to understand their data needs and deliver solutions.
- Performance Optimization: Continuously monitor and optimize data pipelines and analytics processes for efficiency and scalability.
- Adhoc Data Analysis and Reporting/Dashboard Development: Perform exploratory data analysis, develop data visualizations, and generate actionable insights to support business decision-making.
- 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
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