Job description

Overview Black Canyon Consulting (BCC) is searching for a Data Engineer to support the design, development, and maintenance of data infrastructure that enables artificial intelligence, machine learning, and analytical workloads. The Data Engineer will help ensure that data is reliable, accessible, scalable, and operationally ready throughout the full lifecycle from ingestion through deployment. Job Description The Data Engineer will design, implement, maintain, and optimize data pipelines, APIs, data transformation workflows, and supporting infrastructure. This position will work with databases, data lakes, data warehouses, feature stores, and other data platforms to ensure information can be efficiently ingested, transformed, accessed, and used by analytical and AI-enabled systems. The Data Engineer will also develop and maintain ETL/ELT processes, feature engineering workflows, and service layers that expose data or model endpoints to downstream systems and applications. Duties and Responsibilities Develop and maintain data ingestion and transformation pipelines using Python and SQL. Implement bronze, silver, and gold data processing patterns using DuckDB and Apache Iceberg. Build reusable, metadata-driven capabilities for ingesting data from databases, APIs, files, SaaS platforms, CDC, and event-driven sources. Develop configuration-driven source onboarding and source-to-canonical data mappings. Transform heterogeneous source data into standardized and governed data models while maintaining source provenance and traceability. Implement agentic and AI-enabled workflows to support source profiling, schema analysis, data mapping, pipeline generation, testing, troubleshooting, reconciliation, and data quality. Develop automated data validation, reconciliation, testing, monitoring, and error-handling capabilities. Support incremental processing, schema evolution, backfills, and historical data migration. Integrate structured data with related document and geospatial datasets where required. Work with the Data Architect and Lead Data Engineer to implement common engineering standards and reusable platform patterns. Participate in code reviews, automated testing, CI/CD, technical documentation, and operational support. Requirements 4+ years of relevant data engineering or software engineering experience. Strong hands-on experience with Python, SQL, DuckDB, and Apache Iceberg . Experience developing production ETL/ELT pipelines across databases, APIs, files, and semi-structured data sources. Experience implementing bronze/silver/gold or comparable layered data architectures. Experience designing reusable, metadata-driven ingestion and transformation pipelines. Experience with AWS data services, particularly Amazon S3 and AWS Glue. Experience with data modeling, source-to-target mappings, data quality, reconciliation, and schema evolution. Experience developing or implementing agentic or AI-enabled data engineering workflows , including the use of AI agents or LLMs to assist with data engineering tasks. Experience with Git-based development, automated testing, and CI/CD. Bonus Skills Experience with agentic frameworks such as LangChain, LangGraph, or similar orchestration frameworks . Experience with Kafka, Amazon MSK, or CDC technologies. Experience integrating Salesforce, ServiceNow, Microsoft Dataverse, or similar enterprise platforms. Experience with OpenSearch, vector stores, or document-processing pipelines. Experience with GeoParquet, PostGIS, or geospatial datasets. Experience working in the U.S. Federal Government space . Experience with permitting, regulatory, land management, environmental, energy, or natural-resource datasets. Benefits and Salary We attract the best people in the business with our competitive benefits package that includes medical, dental and vision coverage, 401k plan with employer contribution, paid holidays, vacation, and tuition reimbursement. If you enjoy being a part of a high performing, professional service and technology focused organization, please apply today!

Requirements

Must-haves

The Data Engineer will also develop and maintain ETL/ELT processes, feature engineering workflows, and service layers that expose data or model endpoints to downstream systems and applications.
Duties and Responsibilities
Develop and maintain data ingestion and transformation pipelines using Python and SQL.
Implement bronze, silver, and gold data processing patterns using DuckDB and Apache Iceberg.
Build reusable, metadata-driven capabilities for ingesting data from databases, APIs, files, SaaS platforms, CDC, and event-driven sources.
Develop configuration-driven source onboarding and source-to-canonical data mappings.
Transform heterogeneous source data into standardized and governed data models while maintaining source provenance and traceability.
Implement agentic and AI-enabled workflows to support source profiling, schema analysis, data mapping, pipeline generation, testing, troubleshooting, reconciliation, and data quality.
Develop automated data validation, reconciliation, testing, monitoring, and error-handling capabilities.
Support incremental processing, schema evolution, backfills, and historical data migration.
Integrate structured data with related document and geospatial datasets where required.
Work with the Data Architect and Lead Data Engineer to implement common engineering standards and reusable platform patterns.
Participate in code reviews, automated testing, CI/CD, technical documentation, and operational support.
4+ years of relevant data engineering or software engineering experience.
Strong hands-on experience with Python, SQL, DuckDB, and Apache Iceberg .
Experience developing production ETL/ELT pipelines across databases, APIs, files, and semi-structured data sources.
Experience implementing bronze/silver/gold or comparable layered data architectures.
Experience designing reusable, metadata-driven ingestion and transformation pipelines.
Experience with AWS data services, particularly Amazon S3 and AWS Glue.
Experience with data modeling, source-to-target mappings, data quality, reconciliation, and schema evolution.
Experience developing or implementing agentic or AI-enabled data engineering workflows , including the use of AI agents or LLMs to assist with data engineering tasks.
Experience with Git-based development, automated testing, and CI/CD.
Experience with agentic frameworks such as LangChain, LangGraph, or similar orchestration frameworks .

Nice-to-haves

Bonus Skills

Application prep

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Keywords to mirror

machine learningaipythonsqlci/cdaws

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k