Overview
We are seeking a skilled and motivated Data Engineer to join our growing data team. The Data Engineer will be responsible for designing, developing, and maintaining scalable data pipelines and infrastructure to support data analytics and business intelligence initiatives. The ideal candidate will have a strong background in data architecture, ETL (Extract, Transform, Load) processes, database management, and cloud platforms. This role requires a deep understanding of data modeling principles, data integration techniques, and a passion for leveraging data to drive business insights and decision-making.
Roles And Responsibilities - Data Pipeline Development:
- Design, build, and maintain robust and scalable data pipelines to collect, process, and store structured and unstructured data from multiple sources.
- Implement ETL processes to extract data from various systems, transform it into a usable format, and load it into data warehouses or data lakes.
- Optimize data pipeline performance for efficiency, reliability, and data quality using best practices and appropriate technologies.
- Data Modeling and Architecture:
- Design and implement data models, schemas, and database structures to support data storage, retrieval, and analysis.
- Develop and maintain data architecture standards and guidelines to ensure consistency, scalability, and security of data solutions.
- Evaluate and recommend database technologies, tools, and frameworks based on project requirements and data management best practices.
- Data Integration and Transformation:
- Integrate data from disparate sources and systems, ensuring compatibility, consistency, and data integrity throughout the process.
- Transform and cleanse data as needed to support analytics, reporting, and data visualization requirements.
- Implement data quality checks and monitoring to proactively identify and resolve data issues or anomalies.
- Cloud Platform Management:
- Deploy and manage data infrastructure on cloud platforms (e.g., AWS, Azure, Google Cloud), including setting up virtual machines, containers, and serverless computing environments.
- Utilize cloud-based services for data storage, compute, and analytics (e.g., S3, Redshift, BigQuery) to optimize data processing and storage costs.
- Implement security measures and access controls to protect sensitive data and ensure compliance with data privacy regulations.
- Collaboration and Documentation:
- Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver data-driven solutions.
- Document data engineering processes, workflows, and technical specifications to facilitate knowledge sharing and support ongoing maintenance.
- Provide technical guidance and mentorship to junior team members, promoting best practices in data engineering and development.
Compensation
- Competitive base salary commensurate with experience and qualifications.
- Performance-based bonuses or incentives tied to project delivery, data pipeline performance, and business impact.
- Comprehensive benefits package including health insurance, retirement plans, and professional development opportunities.
- Potential for career growth and advancement within the organization based on performance and contributions.