Senior Data Engineer

at  printedcom

You will play a crucial role in designing, building, and maintaining our data platform, with a strong emphasis on streaming data, cloud infrastructure, and machine learning operations.

EXPERIENCE:

• 3+ years of experience in data engineering, with a proven track record in building and maintaining data platforms, preferably on AWS.
• Strong proficiency in Python, experience in SQL and PostgreSQL. PySpark, Scala or Java is a plus.
• Familiarity with Databricks and the Delta Lakehouse concept.
• Experience mentoring or leading junior engineers is highly desirable.

SKILLS:

• Deep understanding of cloud-based data architectures and best practices.
• Proficiency in designing, implementing, and optimizing ETL/ELT workflows.
• Strong database and data lake management skills.
• Familiarity with ML Ops practices and tools, with a desire to expand skills in this area.
• Excellent problem-solving abilities and a collaborative mindset.

• Architect and Implement Data Pipelines:
• Design, develop, and maintain scalable and efficient data pipelines.
• Optimize ETL processes to ensure seamless data ingestion, processing, and integration across various systems.
• Streaming Data Platform Development:
• Lead the development and maintenance of a real-time data streaming platform using tools like Apache Kafka, Databricks, Kinesis.
• Ensure the integration of streaming data with batch processing systems for comprehensive data management.
• Cloud Infrastructure Management:
• Utilize AWS data engineering services (including S3, Redshift, Glue, Kinesis, Lambda, etc.) to build and manage our data infrastructure.
• Continuously optimize the platform for performance, scalability, and cost-effectiveness.
• Communications:
• Collaborate with cross-functional teams, including data scientists and BI developers, to understand data needs and deliver solutions.
• Leverage the project management team to coordinate project, requirements, timelines and deliverables, allowing you to concentrate on technical excellence.
• ML Ops and Advanced Data Engineering:
• Establish ML Ops practices within the data engineering framework, focusing on automation, monitoring, and optimization of machine learning pipelines.
• Data Quality and Governance:
• Implement and maintain data quality frameworks, ensuring the accuracy, consistency, and reliability of data across the platform.
• Drive data governance initiatives, including data cataloguing, lineage tracking, and adherence to security and compliance standards.