Senior Data Engineer III

Responsibilities

• Platform & Architecture
• Scoping, architecting, designing, and developing robust data engineering solutions—including data pipelines, data integration, and infrastructure.
• Support the data architect in the creation of conceptual and logical data models.
• Create physical data models optimized for analytics, reporting, and AI/machine learning use cases.
• Maintain the data platform—making architectural decisions, maintaining high code quality, and delivering scalable, reliable solutions.
• Pipeline Development & Data Integration
• Integrate data from diverse sources, including databases, APIs, flat files, and cloud platforms.
• Design and build performant, scalable data pipelines using tools like dbt, Fivetran, and Airflow.
• Troubleshoot issues with production data pipelines and implement monitoring and alerting as needed.
• Deliver curated datasets to support analytics engineers in building AI and BI solutions.
• Collaboration & Data Quality
• Collaborate across business, governance, QA, and analytics teams to ensure data quality, consistency, and successful solution delivery.
• Implement data quality frameworks and automated tests to ensure integrity, trust, and traceability across the pipeline.
• Technology Best Practices & Innovation
• Define and implement enterprise scale data engineering best practices, standards, and guidelines across the development life cycle.
• Stay up to date on the latest data engineering trends and technologies, advocate for new technologies, and champion their adoption to continuously improve our data infrastructure.

Qualifications

• Education: Bachelor’s degree in computer science, data science, software engineering, information systems, or related quantitative field; master’s degree preferred.
• Experience: 10+ years of Data Engineering experience, with at least 3 years in modern cloud/data stack. Demonstrated experience designing and implementing enterprise scale data platforms.
• Technical Proficiency: Proficient in data management disciplines, including data integration, modeling, building data warehouses/lakes, and data quality, or other areas relevant to data engineering responsibilities and tasks.
• Communication & Mindset: Strong communication skills, to be able to clearly articulate technical concepts to non-technical stakeholders. Strong problem-solving skills and a proactive, ownership-driven mindset.
• Skills: Proficiency in the design and implementation of modern data architectures such as cloud services (AWS, Azure, GCP) and modern data warehouse technologies (Snowflake, Databricks, Redshift, BigQuery). Experience with AWS and Snowflake preferred. Experience with ETL/ELT design and development using tools like Informatica, Matillion, AWS Glue, or equivalent. Experience with Fivetran, dbt preferred. Strong experience with database/big data technologies such as Oracle, SQL Server, Teradata, Apache Spark, Delta Lake, Hadoop. Experience with DevOps/DataOps, Continuous Integration and Continuous Delivery (CI/CD) principles/technologies using tools such as BitBucket, Jenkins, or similar. Experience with Agile methodologies, common scrum practices and tools. Familiarity with data quality and testing frameworks (e.g., dbt tests, Great Expectations, Soda) is a plus.