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Data Engineer - AI Practice Team

LinkedIn ABS Group Houston, TX
Not Applicable Posted March 26, 2026 Job link
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Requirements
  • 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.
  • 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.
Preferred Skills
  • 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).
  • Familiarity with AI/ML data patterns, including feature engineering, embeddings, vector databases, and retrieval-augmented generation (RAG) pipelines.
  • 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.
Education
  • (Not required) – Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a closely related technical field; Master’s degree preferred.