Senior ML/ Data Engineer

Insight Global is seeking a Senior Machine Learning / Data Engineer to join a leading Financial Services client’s AI Product team. This individual will be responsible for building and productionizing machine learning solutions, developing scalable data products, and enabling AI-driven business outcomes through strong data architecture and engineering practices. The ideal candidate has deep experience with Python, Snowflake, DBT, data modeling, and modern data warehousing, and can independently take AI and ML initiatives from concept to production., Design, build, and maintain scalable data pipelines and data products that support machine learning and AI initiatives.

-Productionize machine learning models and partner closely with Data Scientists to deploy, monitor, and optimize models in production environments.

-Develop and manage robust ELT/ETL processes using Python, DBT, and Snowflake.

-Design and implement dimensional models, semantic layers, and scalable data warehouse solutions to support analytics and AI use cases.

-Build data quality, governance, lineage, and monitoring capabilities to ensure reliable and trusted data assets.

-Create and optimize datasets for machine learning training, inference, feature engineering, and model evaluation.

-Collaborate with Data Science, Product, and Business stakeholders to identify opportunities for new AI-driven products and solutions.

-Drive performance improvements and innovation across existing data platforms and machine learning workflows.

-Own projects end-to-end with minimal oversight while maintaining high standards for delivery, scalability, and reliability.

-Contribute to the continuous improvement of data architecture, engineering practices, and AI enablement across the organization.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

8+ years of experience in Data Engineering, Machine Learning Engineering, or related fields (5-6 years considered for exceptional candidates).

• Strong Python development experience for data engineering, machine learning, and automation.

• Hands-on experience productionizing machine learning models and supporting AI solutions in enterprise environments.

• Advanced SQL skills and extensive Snowflake experience (required).

• Strong DBT experience, including data transformations, testing, documentation, and model governance (required).

• Deep expertise in data warehousing, dimensional modeling, and building scalable data products.

• Experience designing and maintaining complex ELT/ETL pipelines.

• Strong understanding of data quality, observability, and governance best practices.

• Ability to work independently, solve complex problems, and deliver results with minimal direction.

• Proven track record of meeting deadlines, driving initiatives forward, and taking ownership of outcomes. - Experience with cloud platforms such as GCP or AWS.

• Familiarity with MLOps concepts, model monitoring, feature stores, and model lifecycle management.

• Experience working with modern orchestration tools such as Airflow or Cloud Composer.

• Exposure to large-scale AI, machine learning, or advanced analytics environments.

• Background building new data products or data platforms from the ground up.