Data Engineer Technology

immediate mission: support the migration of ETL workloads out of Redshift stored procedures and legacy SSIS packages into scalable, maintainable pipelines using AWS Glue and S3. The longer-term vision: help us evolve from batch-oriented processing toward near real-time analytics using stream processing technologies like Apache Flink and ClickHouse, among others. You will be embedded in a cross-functional engineering team, working alongside senior engineers to build pipelines and grow your expertise in modern cloud-native data architecture.

Objectives:

• Build and maintain modern ETL/ELT pipelines using AWS Glue, S3, and related services to help replace legacy stored procedures and SSIS jobs.

• Develop data transformation workflows that are testable, version-controlled, and observable.

• Help maintain our Redshift data warehouse, including materialized views, query performance, and cost efficiency.

• Support the evolution from batch ETL to near real-time stream processing, assisting with the evaluation and implementation of technologies such as Apache Flink, ClickHouse, Kafka, Kinesis, or equivalent platforms.

• Design pipelines that support both near real-time and batch workloads as the platform transitions.

• Collaborate with product and analytics teams to help ensure data models support reporting, AI/ML, and customer-facing features.

• Follow and help refine established patterns and best practices for pipeline development.
• Participate in production support and incident response for data infrastructure.

• Bachelor’s degree in Computer Science, Engineering, or related field. 2-4 years of experience in data engineering roles., * Working experience with AWS Glue (PySpark/Python), S3, and Redshift. Docusign Envelope ID: 3DB5DE32-5404-82BD-80A4-A92CE5E89F74

• Hands-on experience migrating or supporting the migration of ETL workloads from legacy tools (SSIS, stored procedures, or similar) to modern cloud-native pipelines.

• Strong SQL skills with best practices and SQL linting, particularly in Redshift or other columnar/MPP databases.

• Experience with or strong interest in stream processing frameworks (Flink, Spark Streaming, Kafka Streams, or similar).

• Familiarity with data pipeline orchestration, monitoring, and error handling patterns.
• Familiarity with infrastructure-as-code and CI/CD concepts as applied to data pipelines.

Desired Skills:

• Experience with Aurora MySQL or other relational databases, and NoSQL such as DynamoDB.
• Hands-on experience with near real-time OLAP engines (ClickHouse, Apache Druid, or similar).
• Exposure to streaming data infrastructure (Kinesis, Kafka, MQTT).
• Familiarity with IoT or utility/metering data.
• Experience with dbt, Airflow, or Step Functions.

• Strong communication skills, with the ability to explain complex data architecture concepts to technical and non-technical stakeholders.

• A working understanding of software development methodologies (Agile, Scrum, etc.).
• Strong problem-solving abilities and a drive for results.