Senior Data Engineer

The Phoenix Group; New York (NY); On-site; USD 120,000 - 170,000; Full time

Job summary

The Phoenix Group seeks an experienced Data Engineer to design, build, and maintain scalable data pipelines and platforms in a fast-paced, modern data environment. You will work with Snowflake, dbt, and Apache Airflow to optimize data lakes, support reporting and analytics, and collaborate with analysts and stakeholders to translate requirements into robust data solutions.

Strong SQL, Python, and data modeling skills are essential, as is a solid understanding of data governance and quality

Qualifications

• 5+ years of experience in data engineering or a related field.
• Strong SQL and experience with large datasets.
• Hands-on Snowflake, dbt, and Airflow experience.
• Experience building data lakes/warehouses in cloud environments.

Responsibilities

• Design, build, and maintain scalable data pipelines and data workflows across a modern data environment.
• Develop and optimize data solutions using Snowflake, dbt, and Airflow.
• Build and enhance data lakes and data platforms, integrating data from multiple sources.
• Develop reliable ETL/ELT processes to support reporting and analytics.
• Establish data transformation and modeling frameworks using dbt.
• Develop and manage workflow orchestration using Apache Airflow.
• Optimize Snowflake environments for performance, scalability, reliability, and cost.
• Partner with data analysts, technology teams, and business stakeholders to translate requirements into data solutions.
• Implement data quality, monitoring, testing, and governance across pipelines and data products.
• Troubleshoot data pipeline issues and identify opportunities for improvement.
• Contribute to the evolution of the firm's data architecture and engineering standards.
• Clearly communicate technical solutions, project status, risks, and recommendations to technical and non-technical audiences.

Skills

Data engineering

SQL

Python

ETL/ELT

dbt

Airflow

Snowflake

Data modeling

Data governance

Cloud platforms

Communication

Tools

Snowflake

dbt

Apache Airflow

ETL/ELT tools

Python

Job description

• Design, build, and maintain scalable data pipelines and data workflows across a modern data environment.
• Develop and optimize data solutions using Snowflake, dbt, and Airflow.
• Build and enhance data lakes and data platforms, integrating data from a variety of internal and external sources.
• Develop reliable ETL/ELT processes to support reporting, analytics, and downstream applications.
• Establish data transformation and modeling frameworks using dbt.
• Develop and manage workflow orchestration using Apache Airflow.
• Optimize Snowflake environments for performance, scalability, reliability, and cost.
• Partner with data analysts, technology teams, and business stakeholders to understand requirements and translate them into effective data solutions.
• Implement data quality, monitoring, testing, and governance practices across pipelines and data products.
• Troubleshoot data pipeline and platform issues and proactively identify opportunities for improvement.
• Contribute to the ongoing evolution of the firm's data architecture and engineering standards.
• Clearly communicate technical solutions, project status, risks, and recommendations to both technical and non-technical audiences.

Qualifications

• 5+ years of experience in data engineering, data platform engineering, or a related discipline.
• Strong hands-on experience with Snowflake.
• Experience building and maintaining data pipelines using modern ETL/ELT technologies.
• Strong experience with dbt and data transformation/modeling.
• Experience with Apache Airflow or similar workflow orchestration platforms.
• Experience designing and building data lakes, data warehouses, and/or modern cloud data platforms.
• Strong SQL skills and experience working with large, complex datasets.
• Experience with Python or another programming language commonly used in data engineering.
• Understanding of data architecture, data modeling, data quality, and data governance principles.
• Ability to work effectively in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience working within financial services, private equity, investment management, banking, hedge funds, or asset management.
• Experience working with financial or investment-related datasets.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Experience with additional modern data technologies and cloud-based data platforms.
• Experience supporting data initiatives involving analytics, AI, or machine learning.