Senior Data Engineer

Key Responsibilities:
Design, develop, and maintain scalable DBT models that produce reliable datasets, features, and business metrics.
Build and optimize Databricks / PySpark pipelines to process large-scale operational and analytical data.
Develop production-ready workflows using Airflow and Prefect with strong SLA adherence.
Create and maintain Delta/Iceberg tables and lakehouse data architectures.
Partner closely with Data Scientists and ML Engineers to productionize feature engineering pipelines.
Improve performance, scalability, and cost efficiency of Spark and DBT workloads.
Implement strong data quality, testing, documentation, and monitoring practices.
Participate in peer code reviews and contribute to engineering best practices.

Required Skills:
Strong hands-on experience in SQL, DBT, Databricks, PySpark, and Airflow.
Experience working with distributed data systems and large-scale data processing.
Good understanding of data modeling, orchestration, and pipeline optimization.
Experience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or Pulumi.
Familiarity with Delta Lake / Iceberg architectures.
Strong communication and collaboration skills.

Nice to Have:
Experience with Kinesis or other streaming systems.
Exposure to MLOps or ML/AI teams.
Experience working with globally distributed engineering teams.
Familiarity with AI-assisted engineering tools such as Cursor, Copilot, or Claude Code.
Please go through the meeting notes from our client meeting last night, with the hiring managers for the data engineer role.