Job Description
Job Description:
We are seeking an experienced Data Engineer to the
design, development, and optimization of our client data infrastructure. This
role requires deep expertise in cloud technologies (primarily Azure, AWS is a plus) and data engineering best practices, with additional
experience in Apache Spark and Databricks for large-scale data
processing. The Data Engineer will work closely with data scientists, analysts,
and other stakeholders to create scalable and efficient data systems that
support advanced analytics and business intelligence. Additionally, this role involves
mentoring junior engineers and driving technical innovation within the data
engineering team.
Key Responsibilities:
- Collaborate with Solution Architects: Work with Big Data Solution Architects to design, prototype, implement, and optimize data ingestion pipelines, ensuring effective data sharing across business systems.
- ETL/ELT Pipeline Development: Build and optimize ETL/ELT pipelines and analytics solutions using a combination of cloud-based technologies, with an emphasis on Apache Spark and Databricks for large-scale data processing.
- Data Processing with Spark: Leverage Apache Spark for distributed data processing, data transformation, and analytics at scale. Experience with Databricks for optimized Spark execution is highly desirable.
- Production-Ready Solutions: Ensure data architecture, code, and processes meet operational, security, and compliance standards, making solutions production-ready in cloud environments.
- Project Support & Delivery: Actively participate in project and production delivery meetings, providing technical expertise to resolve issues quickly and ensure successful project execution.
- Database Management: Manage both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., DynamoDB, MongoDB) databases, ensuring data is efficiently stored, retrieved, and queried.
- Real-Time Data Processing: Implement and maintain real-time data streaming solutions using tools such as Apache Kafka, AWS Kinesis, or other technologies for low-latency data processing.
- Cloud Monitoring & Automation: Use monitoring and automation tools (e.g., AWS CloudWatch, Azure Monitor) to ensure efficient use of cloud resources and optimize data pipelines.
- Data Governance & Security: Implement best practices for data governance, security, and compliance, including data encryption, access controls, and audit trails to meet regulatory standards.
- Collaboration with Stakeholders: Work closely with data scientists, analysts, and business teams to align data infrastructure with strategic business objectives and goals.
- Documentation: Maintain clear and detailed documentation of data models, pipeline processes, and system architectures to support collaboration and troubleshooting.
Requirements- 5+ years of experience as a Data Engineer, with strong expertise in cloud-based data warehousing, ETL pipelines, and large-scale data processing.
- Proficiency with cloud technologies, with experience in platforms like Azure or AWS.
- Hands-on experience with Apache Spark for distributed data processing and transformation. Experience with Databricks is highly desirable.
- Strong SQL skills and experience with relational databases (e.g., PostgreSQL, MySQL) as well as NoSQL databases (e.g., MongoDB, DynamoDB).
- Proficient in Python for data processing, automation tasks, and building data workflows.
- Experience with PySpark for large-scale data engineering, particularly in Spark clusters or Databricks.
- Experience in designing and optimizing data warehouse architectures, ensuring optimal query performance in large-scale environments.
- A strong understanding of data governance, security, and compliance best practices, including encryption, access control, and data privacy.
Preferred Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Certifications in Data Engineering from cloud providers (e.g., AWS Certified Big Data - Specialty, Microsoft Certified: Azure Data Engineer Associate) are a plus.
- Experience with advanced data engineering tools and platforms such as Databricks, Apache Spark, or similar distributed computing technologies
