Senior Data Engineer in Analytics

Responsibilities

• Data Warehousing: Ingest data from Postgres, Google Cloud Storage, Salesforce, Jira, web analytics platforms, and other sources into BigQuery. Use Data Build Tool (dbt) to transform raw data into user friendly “data-marts” for use in BI tools. Implement reverse-etl to make aggregate data available in the application database for web application and mobile application use.
• Data Quality & Governance: Use dbt to establish a robust data testing framework that ensures data remains accurate and trustworthy, while also producing a comprehensive suite of metric definitions and documentation accessible to anyone in the organization. Champion DataOps best practices across teams to cultivate a consistent, high-quality approach to data management and analytics.
• Evaluate New Tools and Integrations: Help evaluate and choose the best cloud tools to meet ever-evolving business needs.
• Optimizing and Monitoring Cloud Costs: Monitor and implement controls to ensure data warehousing costs arepredictable and within reasonable levels. Unlock synergies between the application database and data warehouse to reduce overall cloud costs.
• Security & Compliance: Help ensure account permissions remain appropriate and enforced across the data consumption layer. Implement controls to ensure the data warehouse maintains compliance with GDPR, PII laws, SOC 2,  and other regulations

Qualifications

• Technical Qualifications: A bachelor’s or master’s degree in Computer Science (or a related field) is highly recommended but not required. The role requires at least three years of experience with SQL, BigQuery, and GCP. Hands-on expertise with dbt is highly preferred, as is experience interfacing with Postgres. Familiarity with ingesting data via APIs from external software platforms and transforming non-tabular data using Python is a plus. Experience with data streaming is also nice to have.
• Experience & Abilities: Experience implementing and taking full ownership for data warehouses is essential, alongside a proven track record of data modeling for analytical use cases. The ideal candidate will have a background in empowering peers through DataOps best practices, with a strong emphasis on data security and governance. Additionally, a deep understanding of the strengths and weaknesses of both transactional and analytical databases is critical for success in this role.
• Soft Skills: Candidates should have excellent communication skills to effectively convey complex technical concepts across cross-functional teams. An ideal candidate is autonomous and independent, exhibiting a strong sense of ownership and accountability. Additionally, they must be able to evangelize and champion best practices across the organization, ensuring alignment and consistent adoption of best practices.