AI Data Engineer

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We are seeking a Data Engineer to help advance our analytics infrastructure, data pipelines, and reporting capabilities. This role will work closely with the Manager of Data Science & AI Engineering to manage and optimize our SQL environments, develop automated data flows, and modernize our Power BI ecosystem.

You'll be responsible for maintaining the integrity and performance of our data systems, enabling scalable and reliable analytics across the organization. The ideal candidate combines strong SQL engineering skills with a deep understanding of data modeling, ETL, and business intelligence.

• Maintain and optimize large SQL Server and AWS RDS databases, ensuring performance, reliability, and scalability.
• Design, build, and manage ETL pipelines and data integrations using AWS Glue, Lambda, and related AWS services.
• Develop and enhance Power BI datasets, models, and dashboards with an emphasis on usability, governance, and performance.
• Partner with business stakeholders to translate requirements into well‑designed data models and reporting solutions.
• Implement best practices for database indexing, query optimization, and data lifecycle management.
• Support the deployment of data‑driven and AI‑enabled applications alongside the Data Science & AI Engineering team.
• Contribute to data quality monitoring, metadata management, and documentation of data sources and transformations.
• Assist in modernizing legacy reporting workflows and migrating data assets to AWS cloud platforms.

• 3–5 years of experience as a Data Engineer, Data Scientist, or Database Developer.
• Advanced proficiency in SQL and database administration (SQL Server, AWS RDS).
• Hands‑on experience building ETL pipelines with AWS Glue, Lambda, or equivalent tools.
• Strong experience with Power BI (data modeling, DAX, Power Query, and performance tuning).
• Familiarity with Python for scripting and automation.
• Understanding of modern data architectures (star/snowflake schemas, data lakes, and cloud‑native data design).
• Solid grasp of version control (Git) and CI/CD principles for data deployments.
• Ability to collaborate across technical and business teams to deliver reliable, scalable analytics.
• Experience handling sensitive data through isolation and anonymization.

• Experience with AWS analytics stack (S3, Glue Data Catalog, Athena, Redshift, or Bedrock).
• Exposure to data orchestration tools (Airflow, Step Functions, or Databricks Workflows).
• Familiarity with API‑based integrations and data ingestion from SaaS systems (e.g., Hub Spot, Unanet, or Salesforce).
• Strong understanding of data governance, security, and change management within analytics environments.
• Interest in or exposure to machine learning, AI automation, or predictive analytics workflows.

• Databases: AWS RDS, SQL Server
• Cloud: AWS (Glue, S3, Lambda, Bedrock, Sage Maker)
• Languages:
  
  SQL, Python
• Analytics:
  Power BI, AWS Quick Sight
• ETL / Pipelines: AWS Glue, Python scripts, SQL stored procedures

Mid‑Senior level

Full‑time

Information Technology

Software Development

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