Sr. Data Engineer

Software Guidance & Assistance, Inc., (SGA), is searching for a Sr. Data Engineer for a contract assignment with one of our premier Insurance clients in Hartford, CT or Charlotte, NC
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• Modernize legacy data assets by migrating and re-engineering them into modern cloud solutions (AWS, Google Cloud Platform, Snowflake) for scalability, security, and cost efficiency.

• Design, develop, and optimize ETL/ELT pipelines for structured and unstructured data, ensuring resilience and high performance.

• Build and manage data pipelines leveraging cloud services such as AWS Glue, EMR, Redshift;
  Google Cloud Platform Big Query, Dataflow; and Snowflake.

• Curate and publish Data Products to support analytics, visualization, machine learning, and Generative AI use cases.

• Implement Data Ops practices for automated deployments, CI/CD integration, and continuous delivery of data solutions.

• Apply best practices for data modeling, governance, and security, ensuring compliance with enterprise standards and regulatory requirements.

• Establish and enforce Data Governance frameworks, including:

  • Data Quality Management
  • Metadata Management
  • Data Lineage Tracking

• Integrate AI/ML models into data pipelines, enabling real‑time scoring, feature engineering, and model retraining workflows.

• Enable Generative AI capabilities by embedding LLMs into data workflows for intelligent automation and advanced insights.

• Develop and deploy AI Agents for automated decision‑making and conversational analytics.

• Lead Proof of Concepts (POCs) and pilot initiatives for emerging technologies‑beyond GenAI‑such as real‑time streaming, AI agents, and next‑gen data platforms.

• Develop and maintain BI dashboards and visualization solutions using tools like Power BI or Tableau to deliver actionable insights.

• Monitor and fine‑tune data pipelines for performance, scalability, and reliability using advanced observability tools.

• Automate auditing, reconciliation, and data quality checks to maintain high data integrity.

• Develop self‑healing pipelines with robust re‑startability mechanisms for resilience.

• Schedule and orchestrate complex workflows using tools like MWAA, Autosys, or Control‑M.

• Champion continuous improvement and innovation by adopting emerging technologies in Data Ops, AI/ML, and cloud engineering.

• Bachelor’s or master’s degree in computer science or a related discipline.
• 7 years of experience in data analysis, transformation, and development, with ideally 2 years in the insurance or a related industry.
• 5 years of strong proficiency in SQL, Python, and ETL tools such as Informatica IDMC for data integration and transformation.
• 3 years.e of experience developing and deploying large‑scale data and analytics applications on cloud platforms such as AWS, Google Cloud Platform and Snowflake.
• Experience in small or medium‑scale Generative AI (GenAI) integration within data workflows or enterprise solutions.
• 3 years of expertise in designing and optimizing data models for Data Warehouses, Data Marts, and Data Fabric, including dimensional modeling, semantic layers, metadata management, and integration for scalable, governed, and high‑performance analytics.
• 3 years of experience processing large‑scale structured and unstructured data in both batch and near‑real‑time environments, leveraging distributed computing frameworks and streaming technologies for high‑performance data pipelines.
• 3 years of experience in Agile methodologies, including Scrum and Kanban frameworks.
• 2 years of experience in leveraging Dev Ops pipelines for automated testing and deployment, ensuring continuous integration and delivery of data solutions.
• Experience accessing and retrieving data from disparate large data sources.
• Proficient in data visualization tools such as Tableau and Power BI, with expertise in creating interactive dashboards, reports, and visual analytics to support data‑driven decision‑making.
• Ability to analyze source systems, provide business solutions, and translate these solutions into actionable steps.

• Knowledge of data observability (metrics, tracing, logs) and monitoring frameworks.
• Exposure to metadata management (catalogs, glossary) and data…