Senior Data Engineer  

We are looking for bright and talented Senior Data Engineers to join our dynamic team here at LENA.

As a Data Engineer, you are responsible for the development, implementation, and maintenance of systems and processes that convert raw data to actionable, clean, and consistent information for downstream consumers. This job position focuses on keeping data architectures as abstract and straightforward as possible.

Tasks may include:

• Managing data engineering lifecycles through off-the-shelf products, managed services, and tools;
• Implementing data warehouses, data lakes, and data meshes;
• Developing data models relevant to the core domain of the client;
• Designing and developing orchestration of data engineering lifecycles;
• Reviewing and providing insight to architectural decision records for different systems that generate data;
• Transforming existing data structures for efficient analytical workflows;
• Serving data for downstream consumers, such as analytics and ML;
• Providing mentorship and training for data engineering; and
• Collaborating closely with partners and customers (upstream and downstream) to understand their data, design data structures, and provide functionality that is beneficial to the project.

As part of a new team, you will help lay the culture and technical foundation for the team. You will enjoy growth opportunities working alongside a team that encourages intellectual excitement and a startup working environment. Furthermore, you will provide growth to your team members through mentorship and collaboration. You will have the opportunity to engage with clients and improve in communication, leadership and management skills.

• Minimum of 5 years' experience as a software engineer, database administrator, data engineer, data science, or related fields
• Deep understanding of the different stages of the data engineering lifecycle (Generation, Storage, Ingestion, Transformation, Serving)
• General understanding on the undercurrents across the data engineering lifecycle (Security, Data Management, DataOps, Data Architecture, Orchestration, Software Engineering)
• Advanced level of SQL, and good understanding of NoSQL
• Intermediate level of Python programming, bash, Spark
• Good understanding of object-oriented programming and functional programming
• Intermediate level of orchestration (e.g., Airflow, Dagster, etc.)
• Intermediate understanding of data warehousing and data modeling techniques, such as Kimball or Data Vault
• Intermediate understanding of distributed file systems, and processing data on such systems
• Experience and knowledge on the following are not required, but preferred:
• JVM Language (Java, Scala)
• AWS (Glue, Step Functions, S3, Redshift, etc.)
• Data Structures (Hash Indexes, SSTables, LSM-Trees, B-Trees, etc.)
• Data Tools and Frameworks (Kafka, Great Expectations, Apache Iceberg, Hive, etc.)
• Data/Software Architecture (Distributed Systems, System Design, Event-Driven Architecture, Infrastructure-as-code, etc.)
• SWE Best Practices (Agile, DevOps, Testing, Clean Code, TDD, DDD, etc.)
• Modern Data Stack (ETL tools, Metadata management, Data Transformation frameworks, etc.)