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Entry Level Databricks Architect Jobs (NOW HIRING)

Darwill

Machine Learning Engineer

Villa Park, IL · Hybrid

This is not an entry-level position, and it is not a principal or architect-level role.. Location ... Optimize Databricks jobs for performance, scalability, and cost efficiency * Write and maintain ...

Darwill, Inc.

Machine Learning Engineer

Villa Park, IL · On-site

This is not an entry-level position, and it is not a principal or architect-level role.. Location ... Optimize Databricks jobs for performance, scalability, and cost efficiency * Write and maintain ...

SynergisticIT

Entry-Level Data Analyst

Fremont, CA

Python/Java, Spark, Airflow, Databricks * ETL architecture, Snowflake, S3, BigQuery, Redshift * Infrastructure-as-code and cloud data platforms (AWS, Azure) Why Candidates Choose SynergisticIT (and ...

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Entry Level Databricks Architect information

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How much do entry level databricks architect jobs pay per year?

As of Jun 1, 2026, the average yearly pay for entry level databricks architect in the United States is $128,756.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,000.00 and $166,000.00 per year, depending on experience, location, and employer.

What is the difference between Entry Level Databricks Architect vs Data Engineer?

AspectEntry Level Databricks ArchitectData Engineer
Required CredentialsBasic knowledge of Databricks, cloud platforms, and data architecture; certifications like Databricks Certified AssociateProficiency in SQL, ETL tools, cloud platforms; certifications like AWS or Azure Data Engineer
Work EnvironmentCollaborates with data teams to design data solutions on Databricks platformBuilds and maintains data pipelines, manages data storage and processing
Industry UsageUsed in organizations adopting Databricks for data analytics and AI projectsWidely used across industries for data integration and pipeline development

While both roles involve working with data and cloud platforms, the Entry Level Databricks Architect focuses on designing data solutions specifically on Databricks, whereas Data Engineers build and maintain the data pipelines and infrastructure across various platforms. The architect role emphasizes architecture and design, while the data engineer role emphasizes implementation and maintenance.

More about Entry Level Databricks Architect jobs
What cities are hiring for Entry Level Databricks Architect jobs? Cities with the most Entry Level Databricks Architect job openings:
What are the most commonly searched types of Databricks Architect jobs? The most popular types of Databricks Architect jobs are:
What states have the most Entry Level Databricks Architect jobs? States with the most job openings for Entry Level Databricks Architect jobs include:
What job categories do people searching Entry Level Databricks Architect jobs look for? The top searched job categories for Entry Level Databricks Architect jobs are:
Entry Level Databricks Architect jobs near you
Infographic showing various Entry Level Databricks Architect job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $128,756 per year, or $61.9 per hour.
Machine Learning Engineer

Machine Learning Engineer

Darwill, Inc.

Oakbrook Terrace, IL • On-site

Full-time

Posted 11 days ago

Job description

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING)
Overview
Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of complex, data-driven marketing solutions, we help CMOs and marketing leaders drive measurable performance through advanced analytics, automation, and AI-powered insights.
We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional machine learning models (e.g., propensity and segmentation models) while also building and maintaining the core data pipelines on Databricks that power our analytics and modeling platforms.
This role is intentionally scoped for a mid-level engineer: someone with enough experience to work independently and make sound engineering decisions, but who is still hands-on, execution-focused, and eager to grow. This is not an entry-level position, and it is not a principal or architect-level role..
Location
Chicago, IL area (Oak Brook / West Suburbs)
Hybrid work model with 1-2 days onsite per week required
Reports To
VP of Data Engineering & Data Science
Responsibilities / Essential Functions
Data Engineering & Platform Foundations
  • Design, build, and maintain ETL pipelines in Databricks using Spark and Delta Lake
  • Independently implement data transformations, joins, and aggregations across large, multi-source datasets
  • Build and maintain data validation and quality checks to ensure reliability of downstream analytics and ML workflows
  • Optimize Databricks jobs for performance, scalability, and cost efficiency
  • Write and maintain clear technical documentation for data pipelines and tables

ML Engineering & MLOps
  • Partner closely with Data Scientists to support traditional ML model development, including feature engineering, training, validation, and deployment
  • Productionize propensity, ranking, and segmentation models used in large-scale marketing campaigns
  • Build and maintain repeatable ML pipelines for training, batch scoring, and inference
  • Implement model versioning, experiment tracking, and reproducibility standards
  • Support model performance monitoring, drift detection, and retraining cycles

Deployment, Monitoring & Operations
  • Deploy data pipelines and ML workflows into production environments serving millions of records
  • Implement monitoring and alerting for data and ML pipelines
  • Support A/B testing and model performance evaluation in partnership with Data Science
  • Troubleshoot production issues independently and collaborate effectively when escalation is needed

GenAI (Secondary / Directional)
  • Contribute to GenAI initiatives as capacity allows
  • Stay informed on emerging AI technologies and tooling
    (GenAI is not the primary focus of this role today.)

Required Qualifications
Experience
  • 3-6 years of professional experience in machine learning engineering, data engineering, or a closely related role
  • Experience working in production environments with minimal day-to-day supervision
  • Demonstrated ability to collaborate effectively with Data Scientists and translate models into production systems

Technical Skills (Must-Have)
Data Engineering & Platform
  • Apache Spark (PySpark, SparkSQL)
  • Databricks (ETL pipelines, workflows, Delta Lake)
  • Strong SQL skills (complex queries, joins, window functions, optimization)
  • Experience building and maintaining scalable data pipelines

Programming & Machine Learning
  • Python (pandas, numpy, scikit-learn; experience with XGBoost or LightGBM preferred)
  • Feature engineering and data preparation for ML models
  • Working knowledge of supervised learning models (classification, regression, ranking)

MLOps & Production
  • Experience deploying ML models into production
  • Model versioning and experiment tracking (e.g., MLflow or similar)
  • Monitoring data quality and model performance in production
  • Supporting retraining and validation workflows

Cloud & Tooling
  • Experience with a major cloud platform (Databrick, AWS)
  • Familiarity with workflow orchestration tools (Databricks Workflows or similar)

Preferred Qualifications (Nice-to-Have)
  • Experience with propensity modeling, customer segmentation, or marketing analytics
  • Exposure to CI/CD concepts for data and ML pipelines
  • Experience with Docker or containerized deployments
  • Exposure to GenAI, LLMs, or RAG-based systems
  • Master's degree in Computer Science, Statistics, or a related field

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