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Data Engineer, AI Support
Insurance Institute for Business & Home Safety - IBHS. Work closely with AI researchers and Data Engineering to prepare, organize, and improve data for research and machine learning projects.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building ETL/data pipelines and data workflows for AI/ML applications, with strong proficiency in Python and SQL. Capable of ensuring data quality, consistency, and reproducibility while effectively communicating insights and collaborating across teams.
Highest-signal resume keywords
ETL/Data Pipeline DevelopmentPython Data ManipulationSQL and NoSQL KnowledgeData Quality and Schema ValidationCloud Data Platforms (Azure, AWS, Google Cloud)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
ETL/Data Pipeline DevelopmentPython Data ManipulationSQLNoSQLData ModelingData Quality AssuranceData VersioningBash/Shell ScriptingDistributed Data ProcessingExperiment Tracking
Soft Skills
Strong Written CommunicationPresentation SkillsTechnical DocumentationRelationship BuildingSound Judgment
Tools & Technologies
Weights & BiasesLangfuseGitHub ActionsGitLab CITerraformAirflowPrefectDagsterSparkLabel Studio
Industry Keywords
Artificial IntelligenceMachine LearningData ScienceData AnnotationMultimodal DatasetsGraph DatabasesMetadata ManagementData Lifecycle ManagementResearch DatasetsCloud Services
Tech Stack
Tools & technologiesAirflowAWSAzureCloudETLLinuxNoSQLPythonShell ScriptingSparkSQLTerraform
About the role
Key responsibilities & impact- Work closely with AI researchers and Data Engineering to prepare, organize, and improve data for research and machine learning projects.
- Build reliable data workflows that move research data from raw sources into usable datasets for analysis, experimentation, training, and evaluation.
- Explore datasets, identify quality issues or gaps, and determine how to structure and use the data.
- Prepare datasets for language, vision, multimodal, and retrieval-based AI applications.
- Ensure research datasets are consistent, traceable, reproducible, and well documented.
- Automate recurring data preparation and processing tasks.
- Develop summaries and visualizations to help teams understand datasets, patterns, and issues.
- Support data integration across research tools, internal systems, and AI platforms.
- Protect sensitive information and follow data handling practices throughout the data lifecycle.
- Contribute to an experimental research environment with evolving datasets, methods, and requirements.
- Stay current on developments in artificial intelligence, machine learning, data science, and emerging analytical technologies.
Requirements
What you’ll need- Bachelor’s degree in data science, statistics, computer science, mathematics, engineering, or a related quantitative field.
- Experience building ETL/data pipelines to clean, transform, integrate, and prepare structured, semi-structured, and unstructured data for AI/ML workflows.
- Strong Python data manipulation skills, including efficient use of vectorized libraries for large-scale data processing, exploration, and visualization.
- Working knowledge of SQL, NoSQL, data modeling, columnar formats, and modern data storage technologies.
- Familiarity with preparing and versioning LLM/VLM training and evaluation datasets, including QA, preference/RL, multimodal, and human-annotated data.
- Familiarity with embedding pipelines, vector databases, semantic search, RAG, and metadata-aware retrieval workflows.
- Exposure to graph databases, knowledge graphs, and graph-based data modeling for AI applications.
- Understanding of data quality, schema validation, dataset versioning, metadata, lineage, and reproducible train/validation/test splits with leakage prevention.
- Familiarity with distributed data processing and workflow orchestration concepts such as DAGs, task dependencies, scheduling, and pipeline monitoring.
- Comfortable working in Linux environments with Bash/shell scripting and basic automation.
- Familiarity with experiment tracking and LLM observability tools such as Weights & Biases and Langfuse.
- Basic understanding of PII handling, masking, hashing, tokenization, and de-identification within data pipelines.
- Familiarity with CI/CD and infrastructure automation tools such as GitHub Actions, GitLab CI, and Terraform.
- Comfortable working with research datasets that may be incomplete, inconsistent, or evolving, and able to investigate the data before implementing a solution.
- Strong written communication, presentation, and technical-documentation skills.
- Ability to build effective working relationships across business and technical functions.
- Ability to exercise sound judgment, manage multiple priorities, and work independently while contributing to cross-functional initiatives.
- Experience supporting AI/ML research, scientific computing, or other data-intensive research environments.
- Hands-on experience with cloud data platforms or services in Azure, AWS, or Google Cloud.
- Experience with data orchestration and distributed processing tools such as Airflow, Prefect, Dagster, Spark, or similar technologies.
- Familiarity with data annotation and human-in-the-loop platforms such as Label Studio or Prodigy, particularly for machine learning or multimodal datasets.
Benefits
Comp & perks- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary & AD&D)
- Paid Time Off (Vacation, Sick & Public Holidays)
- Family Leave (Maternity, Paternity)
- Short Term & Long Term Disability
- Training & Development