Key Activities:
Data Infrastructure Design and Maintenance:
- Architect, maintain, and enhance analytical and operational services and infrastructure, including data lakes, databases, data pipelines, and metadata repositories, to ensure accurate and timely delivery of actionable insights.

**Collaboration:** 
- Work closely with data science teams to design and implement data schemas and models, integrate new data sources with product teams, and collaborate with other data engineers to implement cutting-edge technologies in the data space.

Data Processing:
- Develop and optimize large-scale batch and real-time data processing systems to support the organization's growth and improvement initiatives.

Workflow Management:
- Utilize workflow scheduling and monitoring tools like Apache Airflow and AWS Batch to ensure efficient data processing and management.

Quality Assurance:
- Implement robust testing strategies to ensure the reliability and usability of data processing systems.

Continuous Improvement:
- Stay abreast of emerging technologies and best practices in data engineering, and propose and implement optimizations to enhance development efficiency.


Required Skills:
Technical Expertise:
- Proficient in Unix environments, distributed and cloud computing, Python frameworks (e.g., pandas, pyspark), version control systems (e.g., git), and workflow scheduling tools (e.g., Apache Airflow).

Database Proficiency:
- Experience with columnar and big data databases like Athena, Redshift, Vertica, and Hive/Hadoop.

Cloud Services:
- Familiarity with AWS or other cloud services like Glue, EMR, EC2, S3, Lambda, etc.

Containerization:
- Experience with container management and orchestration tools like Docker, ECS, and Kubernetes.

CI/CD:
- Knowledge of CI/CD tools such as Jenkins, CircleCI, or AWS CodePipeline.


Nice-to-have Requirements:
Programming Languages:
- Familiarity with JVM languages like Java or Scala.

Database Technologies:
- Experience with RDBMS (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., DynamoDB, Redis).

BI Tools:
- Exposure to enterprise BI tools like Tableau, Looker, or PowerBI.

Data Science Environments:
- Understanding of data science environments like AWS Sagemaker or Databricks.

Monitoring and Logging:
- Knowledge of log ingestion and monitoring tools like the ELK stack or Datadog.

Data Privacy and Security:
- Understanding of data privacy and security tools and concepts.

Messaging Systems:
- Familiarity with distributed messaging and event streaming systems like Kafka or RabbitMQ.