Data Engineer Airflow Jobs in Connecticut (NOW HIRING)

Software/Data Engineer

Location: Connecticut (Hybrid)

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 Software/Data Engineer to help build and operate the Casual Precision Data Platform (CPDP). You'll work across both sides of our 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 a software engineering role focused on building reliable production data systems using Python, Spark, AWS, Airflow, and dbt—not dashboard development, reporting, or analyst work. You'll independently deliver well-defined engineering work, partner with senior engineers on architecture and platform evolution, and contribute across both ingestion and transformation as priorities shift.

What You'll Do

• Design, build, and operate AWS Glue (PySpark) ingestion jobs and Airflow DAGs that load operational and partner data using reusable, configuration-driven patterns.
• Build and maintain dbt models that transform Bronze into trusted Silver and Gold datasets, including tests, documentation, macros, and Airflow/Cosmos orchestration.
• Modernize legacy SQL and data pipelines by migrating priority workloads into dbt and supporting AI-assisted document ingestion workflows.
• Own the day-to-day reliability of assigned pipelines by monitoring production health, investigating failures, reprocessing data when required, and maintaining healthy data contracts between ingestion and transformation.
• Support our event ingestion platform (pixels/identity) by investigating Lambda and Firehose issues, implementing targeted fixes, and protecting downstream data quality.
• Partner with Analytics, BI, and Data Science teams to ensure trusted datasets meet business needs while following platform standards, CI/CD practices, and Dev → Stage → Production deployment processes.
• Participate in code reviews, contribute reusable engineering patterns, and continuously improve the CPDP platform.

Required Skills & Experience

• 3–5 years of experience in software engineering or data engineering with a strong focus on production data systems.
• Strong Python for production applications and data pipelines—not just notebooks.
• Strong SQL including window functions, incremental processing, query optimization, and performance tuning.
• Hands-on experience with Spark and AWS Glue (or equivalent distributed data processing frameworks such as EMR).
• Production experience with dbt, including models, sources, tests, documentation, and reusable macros.
• Experience building and operating Airflow workflows, including scheduling, retries, alerting, and deployment.
• Experience with Redshift or another modern cloud data warehouse.
• Working knowledge of AWS services used in data platforms, including S3, IAM, CloudWatch, and related compute and orchestration services.
• Understanding of Docker containers and how containerized applications
• Familiarity with modern CI/CD practices, Git workflows, automated testing, and pull request-based deployments.
• Experience building reliable production systems with strong logging, monitoring, idempotent processing, auditing, and operational troubleshooting.
• Comfortable reading unfamiliar code, debugging production systems, and balancing ingestion and transformation priorities.

Nice to Have

• Deep AWS Glue/PySpark experience, including partitioning strategies, job tuning, and production debugging.
• Experience with Astronomer Cosmos or advanced dbt package and CI patterns.
• TypeScript and AWS Lambda development.
• Experience with Amazon Bedrock or other LLM-assisted document processing pipelines.
• Tableau or BI experience as a consumer of curated data.
• Familiarity with advertising technology, identity, attribution, or media analytics.
• Exposure to Terraform, GitHub Actions, or other infrastructure automation tools.

What Success Looks Like

Within your first year, you'll be able to:

• Independently deliver production-ready ingestion pipelines and dbt models.
• Own the operational health and reliability of assigned data pipelines.
• Improve platform quality through testing, monitoring, automation, and reusable engineering patterns.
• Participate confidently in technical design discussions and code reviews.
• Contribute to the continued evolution of the Casual Precision Data Platform while helping mentor junior engineers as the engineering team grows.