Lead the design and implementation of scalable, high-performance data architectures that support diverse data sources, including structured, semi-structured, and unstructured data
Architect and build data pipelines that can process and analyze large-scale datasets in real-time and batch modes
Mentor and guide junior data engineers in best practices, code quality, and technical skills, fostering a culture of continuous learning
Ensure the reliability, efficiency, and security of data pipelines by implementing monitoring, alerting, and automated recovery mechanisms
Collaborate with cross-functional teams, including data scientists, analysts, and product managers, to align data solutions with business needs and goals
Lead the adoption of new technologies and tools that enhance the data engineering capabilities of the team
Oversee the development of data models, schemas, and data marts that enable efficient data analysis and reporting
Implement data governance frameworks, including data lineage, metadata management, and data quality standards

Bachelor's or Master's degree in Computer Science, Information Technology, or a related field
5-7 years of experience in data engineering or a related role, with a proven track record of delivering scalable data solutions
Advanced SQL skills, including query optimization, indexing strategies, and data modeling techniques (e.g., star and snowflake schemas)
Expertise in programming languages such as Python, Java, or Scala, with experience in developing complex data processing algorithms
In-depth knowledge of cloud data services and big data technologies, such as Snowflake, AWS EMR, Azure HDInsight, or Google Dataflow
Experience with data streaming technologies, such as Apache Kafka, Apache Flink, or Amazon Kinesis, for real-time data processing
Strong understanding of data lake architectures, data warehousing, and data integration techniques
Proficiency in data pipeline orchestration tools, such as Apache Airflow, Prefect, or Dagster, for managing complex workflows
Knowledge of data security frameworks and compliance standards (e.g., GDPR, HIPAA) and their application in data engineering
Experience with DevOps practices, including CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and containerization (e.g., Docker, Kubernetes)

Senior Data Engineer

Key skills