Developer II at RealPage, Inc. · STPI

Overview

We are seeking an experienced Data Engineer (5+ years) to join our Data Platform team. This role is focused on building, maintaining, and operating high‑quality datasets in our Snowflake data warehouse to support critical downstream consumers such as Analytics, Reporting, AI/ML, and Product teams.

The ideal candidate takes strong ownership of data reliability (“keeping the green light on”), ensures the business continues to run smoothly, and proactively identifies and resolves data issues before they impact consumers. You will work with large‑scale, structured and unstructured datasets, design efficient data models, and build robust, observable data pipelines on the cloud.

Responsibilities

Key Responsibilities:

• Own and maintain high‑quality datasets in the Snowflake data warehouse, ensuring they reliably meet the needs of downstream consumers such as Analytics, Reporting, Product, and AI/ML teams.
• Design, build, and optimize scalable and efficient data models that support analytical and operational use cases.
• Write and optimize complex SQL queries, including multi‑table joins, aggregations, window functions, and performance tuning on large datasets.
• Build, enhance, and maintain robust batch and near‑real‑time data pipelines using Python or similar programming languages.
• Work with large‑scale structured, semi‑structured, and unstructured data (e.g., JSON, event data, logs).
• Develop and operationalize transformation workflows using dbt or similar data transformation tools.
• Leverage cloud services (AWS or equivalent) such as S3, AWS Batch, Lambda, DMS, and Snowflake integrations to build scalable data solutions.
• Implement strong data quality checks, validations, and automated test cases to ensure data accuracy and trust.
• Establish and maintain observability, monitoring, and alerting for data pipelines to proactively detect and resolve issues.
• Perform root cause analysis for data incidents and drive long‑term fixes to prevent recurrence.
• Collaborate closely with Product, Analytics, and other stakeholders to translate business requirements into well‑designed technical data solutions.
• Communicate clearly and effectively with both technical and non‑technical audiences regarding data behavior, limitations, and dependencies.
• Take ownership of data platform components and continuously improve reliability, performance, and maintainability.
• Contribute to team best practices, coding standards, and provide input into architectural decisions for the Data Platform as needed.

Qualifications

Required Qualifications:

• 5+ years of experience as a Data Engineer or in a closely related data‑focused engineering role.
• Strong expertise in SQL, including complex joins, aggregations, window functions, performance tuning, and working with large datasets.
• Proficiency in Python or another programming language used for data engineering and pipeline development.
• Hands‑on experience with Snowflake, including working with core warehouse objects (e.g., tables, views, stored procedures, tasks), or a comparable cloud data warehouse.
• Strong experience designing and managing data transformations using DBT (or equivalent transformation frameworks).
• Experience building and operating data pipelines in a cloud environment (AWS or similar).
• Solid understanding of data modeling, data warehousing concepts, and analytical workloads.
• Strong focus on data quality, reliability, and operational excellence.
• Excellent communication skills and the ability to work independently with product, analytics, and business stakeholders.

Preferred/Nice-to-Have Skills:

• Experience with workflow orchestration tools such as Apache Airflow.
• Experience with Kafka or other streaming/event‑driven data platforms.
• Experience handling large‑scale semi‑structured and unstructured data (e.g., JSON, event data, logs).
• Familiarity with CI/CD practices (e.g., Docker, GitHub Actions) for data pipelines and engineering.
• Exposure to supporting analytics, BI, or AI/ML platforms.
• Experience improving pipeline observability, monitoring, and alerting at scale.