ABS is seeking an exceptional Data Engineer to join us full-time on our Artificial Intelligence (AI) Practice Team. In this role, you will design and operate the data foundations that power AI chat assistants, custom AI models, and AI-driven process optimization for ABS Consulting clients. You will build robust pipelines that integrate structured and unstructured data, standardize and tag enterprise content, and enable scalable, low-latency retrieval for AI workloads.
Location:This position can be based in Houston, Texas; Knoxville, Tennessee; or Washington, DC.
What You Will Do:- Design, build, and maintain scalable ETL/ELT pipelines to ingest, clean, and transform structured and unstructured data for AI assistants and custom models.
- Integrate diverse knowledge repositories (documents, policies, procedures, standards, databases) into centralized data platforms that support retrieval-augmented generation (RAG) and search.
- Implement data standardization, normalization, and tagging pipelines to align content with enterprise taxonomies and ontologies.
- Collaborate with AI/ML engineers to productionize model-ready datasets, feature stores, and embeddings for prediction, classification, and optimization use cases.
- Optimize data workflows for reliability, cost, and performance across batch and streaming workloads, including monitoring, alerting, and capacity planning.
- Establish and enforce data quality, lineage, and governance practices to ensure trustworthy inputs to AI systems and process-automation solutions.
- Automate and templatize common data engineering patterns to accelerate delivery across multiple client engagements and industry domains.
- Partner with consultants and business stakeholders to translate process optimization and analytics requirements into robust, maintainable data solutions.
What You Will Need: Education and Experience
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a closely related technical field; Master's degree preferred.
- 6+ years of professional data engineering experience designing, building, and operating production data solutions.
- Demonstrated experience working in data-intensive environments (e.g., analytics platforms, AI/ML workloads, large-scale content repositories, or enterprise data platforms).
- Hands-on experience delivering solutions on at least one major cloud provider (AWS, Azure, or Google Cloud), including managed data and analytics services.
Knowledge, Skills, and Abilities
- Strong command of SQL and at least one programming language commonly used in data engineering (Python preferred) for building production-grade data pipelines.
- Hands-on experience with modern data processing frameworks and platforms (e.g., Spark, Databricks, Snowflake, BigQuery, Synapse, or similar).
- Proficiency with ETL/ELT orchestration tools and workflows (e.g., Airflow, dbt, Azure Data Factory, AWS Glue, or equivalent).
- Experience designing and operating data lakes/lakehouses and integrating multiple data sources (relational, NoSQL, files, APIs) into cohesive data models.
- Deep experience working with unstructured and semi-structured data (documents, PDFs, JSON, logs), including content extraction, normalization, and metadata/tagging.
- Familiarity with AI/ML data patterns, including feature engineering, embeddings, vector databases, and retrieval-augmented generation (RAG) pipelines.
- Strong understanding of data modeling, data quality, data governance, and lineage practices for regulated or compliance-sensitive environments.
- Proficiency with cloud-native data services (e.g., S3/ADLS/GCS, managed warehouses, streaming services like Kafka/Kinesis/Event Hubs).
- Solid grounding in software engineering best practices (version control, CI/CD, testing, code review) as applied to data engineering.
Reporting Relationships:This role reports to a project manager and does not initially include direct reports.
Salary Range:$95,000 - $150,000 USD
Starting salary is based on multiple factors including skillset and experience.
Notice: This role has been opened for a future need expected within three months of the original posting date of this position. Potential candidates may not receive communication until the open headcount is confirmed.
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