Data Engineer 2

Job Summary

Key Responsibilities

• Automate deployment of distributed systems that ingest and transform relational, event-based, and unstructured data.
• Monitor and troubleshoot data quality and data integrity issues.
• Implement metadata, access, retention, and other data governance processes.
• Develop scalable data pipelines using ETL/ELT tools or scripting languages.
• Develop physical data models and implement data storage architectures.
• Operate large-scale data storage and processing solutions using distributed and cloud-based platforms.
• Use Agile, DevOps, Scrum, Kanban, and continuous improvement practices for data-driven applications.

Core Responsibilities

• Develop and maintain enterprise data products, pipelines, transformations, and curated datasets for reporting, analytics, automation, and GenAI use cases.
• Create reusable, discoverable, and governed data assets with metadata, lineage, and quality controls.
• Collaborate with product, engineering, analytics, and business teams to deliver reliable AI-ready data products.

Competencies

• Translate stakeholder needs into verifiable system requirements and establish acceptance criteria.
• Perform ETL activities from varied sources for downstream applications and users.
• Write and test code, test scripts, and build scripts using version control and automation.
• Apply data quality metrics, SDLC practices, and solution validation testing.
• Document solutions and communicate knowledge to stakeholders.
• Identify data flaws and implement data-based solutions that address systemic root causes.
• Collaborate effectively, communicate clearly, focus on customers, and make timely decisions.

Education and Experience

• A college, university, or equivalent degree in a relevant technical discipline, or relevant equivalent experience, is required.
• Three to six years of relevant experience is stated.
• Relevant temporary student employment, internship, co-op, or extracurricular team experience is preferred.
• Knowledge of current data engineering technologies is highly preferred.

Technical Experience

• Preferred exposure includes big data open source technologies, Spark, Scala or Java, MapReduce, Hive, HBase, Kafka, or equivalent coursework.
• Preferred knowledge includes SQL, clustered cloud-based implementations, large-file movement in cloud environments, Agile development, analytical solutions, and IoT technology.

Required Skills, Education, or Experience

• Experience with data integration, ETL/ELT processes, data pipelines, and data transformations using modern cloud-based data platforms.
• Working knowledge of data modeling, SQL, data quality, metadata management, and data governance.
• Ability to translate business and product requirements into scalable, maintainable, performant, and reusable technical solutions.
• Experience working in Agile cross-functional teams.
• A bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.

Preferred Skills and Experience

• Experience with Data-as-a-Product operating models, data catalogs, lineage, quality frameworks, and certified data products is preferred.
• Exposure to AI/ML or GenAI initiatives and AI-ready datasets, semantic models, knowledge assets, or related data structures is preferred.