Junior Software/Data Engineer

Work Arrangement: Full-time, hybrid. You'll work from our Connecticut office several days each week, collaborating closely with senior engineers while maintaining flexibility to work remotely on other days. About the Role Casual Precision is hiring a Junior Software/Data Engineer to help build and operate the Casual Precision Data Platform (CPDP). You'll work across both sides of the data platform: ingestion (bringing operational and partner data into Bronze) and transformation (building dbt models that turn Bronze into trusted Silver and Gold datasets used for attribution, analytics, and client delivery). This is an engineering growth role focused on production Python, SQL, cloud data engineering, orchestration, and software engineering practices—not dashboard development, BI, or analyst work. You'll work under the mentorship of senior engineers, delivering production code while steadily taking on greater ownership of the platform. What You'll Do Build and maintain AWS Glue (PySpark) ingestion jobs and Airflow DAGs that load data into Bronze (S3/Parquet → Redshift) following established engineering patterns. Build and maintain dbt models that transform Bronze into trusted Silver and Gold datasets, including tests, documentation, and reusable Jinja components. Help migrate well-scoped legacy SQL workflows into dbt and support AI-assisted document ingestion enhancements when assigned. Monitor and troubleshoot data pipelines, dbt runs, and platform health using CloudWatch, Slack, and automated testing; resolve issues using runbooks and elevate when appropriate. Support our event ingestion platform (pixels and identity) by investigating logs, reproducing issues, and implementing small production fixes under review. Validate curated datasets with Analytics and BI teams while following platform standards, CI/CD practices, and Dev → Stage → Production deployment processes. Participate in code reviews, technical discussions, and continuous improvement of the CPDP platform. Required Skills & Experience 1–3 years of experience in software engineering, data engineering, analytics engineering, or equivalent internship or academic project experience. Strong SQL including joins, CTEs, aggregations, and window functions. Working knowledge of Python, including scripting, packaging, and basic testing. Familiarity with Spark concepts through PySpark, AWS Glue, coursework, or personal projects. Exposure to workflow orchestration tools such as Airflow, Prefect, AWS Step Functions, or similar. Comfortable using Git, participating in code reviews, and delivering small, well-tested changes. Strong written communication for tickets, documentation, incident notes, and pull requests. Curious, collaborative, and comfortable learning across both ingestion and transformation engineering. Nice to Have Hands-on AWS Glue or PySpark experience. Experience with dbt (projects, coursework, or internships). Experience with Redshift, Snowflake, BigQuery, or Databricks. Exposure to TypeScript, Node.js, or AWS Lambda. Familiarity with GitHub Actions or other CI/CD tooling. Tableau or BI experience as a consumer of curated datasets. Interest in advertising technology, attribution, identity, or media analytics. What Success Looks Like Within your first year, you'll be able to: Deliver production-ready ingestion jobs and dbt models independently. Diagnose and resolve common pipeline issues with minimal supervision. Write well-tested, documented Python and SQL that follows platform standards. Participate confidently in code reviews and technical discussions. Contribute ideas that improve the reliability, quality, and scalability of the platform. #J-18808-Ljbffr CASUAL PRECISION LLC