Data Engineer AI/ML & Advanced Analytics
Q1 Technologies, Inc.
Aurora, IL, United States
14 days ago
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Role details
Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
4 years minimum
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Amazon Web Services
Data Analysis
Automation of Tests
Microsoft Azure
Big Data
Clinical Data Repository
Code Review
Information Systems
Continuous Integration
Data Architecture
Information Engineering
+33 more
Data Governance
Extract Transform Load (ETL)
Data Transformation
Data Security
Dataspaces
Data Systems
Data Warehousing
Distributed Computing Environment
Github
Python (Programming Language)
Machine Learning
Meta-Data Management
Scrum Methodology
Cloud Services
DataOps
Azure Machine Learning
Scala (Programming Language)
SQL Databases
Feature Store
Data Processing
Feature Engineering
Azure Data Factory
Apache Spark
Agentic-AI
Data Lakes
Pyspark
Information Technology
Data Lineage
Real Time Data
Data Management
Machine Learning Operations
Data Pipelines
Databricks
Job description
| Job Description:**Please strictly adhere to the following resume naming convention: ALL CAPS, NO SPACES B/T UNDERSCORES PTN_US_GBAMSREQID_CandidateBeelineID i.e. PTN_US_9999999_SKIPJOHNSON0413 MSP Owner: William Bristol Location: See Below Duration: 6 months Data Engineer - AI/ML & Advanced Analytics Position Summary We are seeking a highly skilled Data Engineer to support enterprise AI/ML and Advanced Analytics initiatives by building scalable, reliable, and high-performance data platforms and pipelines. This role will focus on designing and engineering modern data solutions that enable machine learning, advanced analytics, and AI-driven decision making. The ideal candidate has strong expertise in data architecture, cloud platforms, large-scale data processing, and data engineering best practices. The successful candidate will build and optimize data ecosystems that empower data scientists, analysts, and business teams to generate actionable insights and operational value. Key Responsibilities · Design, develop, and maintain scalable data pipelines supporting AI/ML and analytics workloads. · Build and optimize batch and real-time data ingestion frameworks. · Develop data integration solutions across multiple internal and external data sources. · Engineer reliable datasets and feature stores that support machine learning model development. · Implement data transformation, cleansing, enrichment, and validation processes. · Design and maintain modern data lake, lakehouse, and data warehouse architectures. · Ensure data quality, integrity, security, and governance standards are met. · Optimize data processing performance, scalability, and operational efficiency. · Collaborate with Data Scientists and Analysts to support feature engineering and model deployment requirements. · Enable MLOps and ML platform capabilities to support model operationalization. · Implement monitoring, observability, and operational support processes for data platforms. · Maintain documentation, data lineage, and metadata management standards. Required Qualifications · Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Engineering, or related field. · 4+ years of experience in Data Engineering, Data Platforms, or Analytics Engineering. · Strong proficiency in Python, SQL, Spark, Scala, or equivalent technologies. · Experience building cloud-native data solutions using Azure, AWS, or GCP. · Experience with ETL/ELT frameworks and large-scale data processing. · Knowledge of distributed data processing technologies and modern data architectures. · Experience with data lake, warehouse, and lakehouse platforms. · Understanding of data governance, security, and data quality practices. · Strong collaboration and problem-solving skills. Role Descriptions: Design and develop reusable data engineering components | templates | and standards for enterprise-wide use.Build and optimize data processing pipelines using Python | PySpark | and Databricks (Delta Lake | workflows | jobs | notebooks | Unity Catalog).Strong AWS experience across compute | storage | networking | and orchestration services.Triage | diagnose | and resolve complex data processing and pipeline failures with strong analytical skills.Mentor and guide developers across multiple Scrum teams | providing technical leadership | code reviews | and architectural direction.Produce clear | thorough documentation of code | patterns | and solution designs for enterprise reuse.Model and transform complex healthcare datasets with strong understanding of claims | eligibility | provider | and other payer data.Experience with CICD | automated testing | and job promotion using GitHub Actions.Ensure data security | governance | lineage | and HIPAAPHI compliance in all solutions.Participate in AgileScrum ceremonies | lead design discussions and support story refinement. Essential Skills: Data Engineering | Databricks | PySpark | SQLmust have experience on agentic AI Desirable Skills: Keyword: Skills: Digital : Databricks~Digital : PySpark~AI Agents~Azure Data Factory Experience Required: 4-6Comments for Suppliers:two positions open one beeline ID released to avoid duplicates |
| Job Description:**Please strictly adhere to the following resume naming convention: ALL CAPS, NO SPACES B/T UNDERSCORES PTN_US_GBAMSREQID_CandidateBeelineID i.e. PTN_US_9999999_SKIPJOHNSON0413 MSP Owner: William Bristol Location: See Below Duration: 6 months Data Engineer - AI/ML & Advanced Analytics Position Summary We are seeking a highly skilled Data Engineer to support enterprise AI/ML and Advanced Analytics initiatives by building scalable, reliable, and high-performance data platforms and pipelines. This role will focus on designing and engineering modern data solutions that enable machine learning, advanced analytics, and AI-driven decision making. The ideal candidate has strong expertise in data architecture, cloud platforms, large-scale data processing, and data engineering best practices. The successful candidate will build and optimize data ecosystems that empower data scientists, analysts, and business teams to generate actionable insights and operational value. Key Responsibilities · Design, develop, and maintain scalable data pipelines supporting AI/ML and analytics workloads. · Build and optimize batch and real-time data ingestion frameworks. · Develop data integration solutions across multiple internal and external data sources. · Engineer reliable datasets and feature stores that support machine learning model development. · Implement data transformation, cleansing, enrichment, and validation processes. · Design and maintain modern data lake, lakehouse, and data warehouse architectures. · Ensure data quality, integrity, security, and governance standards are met. · Optimize data processing performance, scalability, and operational efficiency. · Collaborate with Data Scientists and Analysts to support feature engineering and model deployment requirements. · Enable MLOps and ML platform capabilities to support model operationalization. · Implement monitoring, observability, and operational support processes for data platforms. · Maintain documentation, data lineage, and metadata management standards. Required Qualifications · Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Engineering, or related field. · 4+ years of experience in Data Engineering, Data Platforms, or Analytics Engineering. · Strong proficiency in Python, SQL, Spark, Scala, or equivalent technologies. · Experience building cloud-native data solutions using Azure, AWS, or GCP. · Experience with ETL/ELT frameworks and large-scale data processing. · Knowledge of distributed data processing technologies and modern data architectures. · Experience with data lake, warehouse, and lakehouse platforms. · Understanding of data governance, security, and data quality practices. · Strong collaboration and problem-solving skills. Role Descriptions: Design and develop reusable data engineering components | templates | and standards for enterprise-wide use.Build and optimize data processing pipelines using Python | PySpark | and Databricks (Delta Lake | workflows | jobs | notebooks | Unity Catalog).Strong AWS experience across compute | storage | networking | and orchestration services.Triage | diagnose | and resolve complex data processing and pipeline failures with strong analytical skills.Mentor and guide developers across multiple Scrum teams | providing technical leadership | code reviews | and architectural direction.Produce clear | thorough documentation of code | patterns | and solution designs for enterprise reuse.Model and transform complex healthcare datasets with strong understanding of claims | eligibility | provider | and other payer data.Experience with CICD | automated testing | and job promotion using GitHub Actions.Ensure data security | governance | lineage | and HIPAAPHI compliance in all solutions.Participate in AgileScrum ceremonies | lead design discussions and support story refinement. Essential Skills: Data Engineering | Databricks | PySpark | SQLmust have experience on agentic AI Desirable Skills: Keyword: Skills: Digital : Databricks~Digital : PySpark~AI Agents~Azure Data Factory Experience Required: 4-6Comments for Suppliers:two positions open one beeline ID released to avoid duplicates |
Requirements
· Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Engineering, or related field.
· 4+ years of experience in Data Engineering, Data Platforms, or Analytics Engineering.
· Strong proficiency in Python, SQL, Spark, Scala, or equivalent technologies.
· Experience building cloud-native data solutions using Azure, AWS, or GCP.
· Experience with ETL/ELT frameworks and large-scale data processing.
· Knowledge of distributed data processing technologies and modern data architectures.
· Experience with data lake, warehouse, and lakehouse platforms.
· Understanding of data governance, security, and data quality practices.
· Strong collaboration and problem-solving skills.
Role Descriptions: Design and develop reusable data engineering components| templates| and standards for enterprise-wide use.Build and optimize data processing pipelines using Python| PySpark| and Databricks (Delta Lake| workflows| jobs| notebooks| Unity Catalog).Strong AWS experience across compute| storage| networking| and orchestration services.Triage| diagnose| and resolve complex data processing and pipeline failures with strong analytical skills.Mentor and guide developers across multiple Scrum teams| providing technical leadership| code reviews| and architectural direction.Produce clear| thorough documentation of code| patterns| and solution designs for enterprise reuse.Model and transform complex healthcare datasets with strong understanding of claims| eligibility| provider| and other payer data.Experience with CICD| automated testing| and job promotion using GitHub Actions.Ensure data security| governance| lineage| and HIPAAPHI compliance in all solutions.Participate in AgileScrum ceremonies| lead design discussions and support story refinement. Essential Skills: Data Engineering| Databricks| PySpark| SQLmust have experience on agentic AI Desirable Skills: Keyword: Skills: Digital : Databricks~Digital : PySpark~AI Agents~Azure Data Factory Experience Required: 4-6 Comments for Suppliers: two positions open one beeline ID released to avoid duplicates
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