Job Description
Location: Chicago-IL - Hybrid 3 days at On-site
Duration: 6 months
Role Descriptions:
Key Responsibilities
· Pipeline Development – Design, implement, and optimize scalable ETL/ELT pipelines using Databricks (PySpark, SQL, Delta Lake) to ingest structured and semi structured data from multiple sources (APIs, databases, streaming).
· Data Warehousing – Build and maintain cloud data warehouse solutions (Snowflake / Azure Synapse) – design star schemas, fact/dimension tables, and aggregate tables for high performance reporting.
· Data Modeling – Create logical and physical data models for operational and analytical use cases; implement SCD Type 2, slowly changing dimensions, and data vault methodologies where appropriate.
· Performance Tuning – Optimize Spark jobs, SQL queries, and data partitioning strategies to handle petabyte scale data with low latency.
· Governance & Quality – Implement data quality checks, monitoring, and lineage using tools like Great Expectations or custom frameworks; enforce data governance policies (GDPR/CCPA).
· Collaboration – Partner with data analysts, product managers, and engineers to translate business requirements into technical data solutions.
· CI/CD & Automation – Automate deployment of data pipelines using Azure DevOps or GitHub Actions; maintain infrastructure as code (Terraform) for data resources.
Required Skills & Experience
· Total Experience: 10+ years in data engineering or related roles.
· Cloud Data Platforms: Deep hands on experience with Databricks (notebooks, jobs, clusters, Delta Lake, Unity Catalog) – must have production level work.
· Data Warehousing: Proven experience with cloud data warehouses (Snowflake, Azure Synapse, or Redshift) – design, optimisation, and administration.
· Data Modeling: Strong knowledge of dimensional modeling (Kimball/Inmon), relational database design, and experience with tools like ER/Studio or dbt.
· Programming: Expert in Python and SQL – ability to write maintainable, production grade code.
· Big Data: Hands on with Apache Spark (PySpark), distributed computing, and performance tuning.
· Orchestration: Experience with workflow tools (Airflow, Azure Data Factory, or Prefect) for scheduling and monitoring pipelines.
· Version Control: Proficient with Git and collaborative development workflows.
Duration: 6 months
Role Descriptions:
Key Responsibilities
· Pipeline Development – Design, implement, and optimize scalable ETL/ELT pipelines using Databricks (PySpark, SQL, Delta Lake) to ingest structured and semi structured data from multiple sources (APIs, databases, streaming).
· Data Warehousing – Build and maintain cloud data warehouse solutions (Snowflake / Azure Synapse) – design star schemas, fact/dimension tables, and aggregate tables for high performance reporting.
· Data Modeling – Create logical and physical data models for operational and analytical use cases; implement SCD Type 2, slowly changing dimensions, and data vault methodologies where appropriate.
· Performance Tuning – Optimize Spark jobs, SQL queries, and data partitioning strategies to handle petabyte scale data with low latency.
· Governance & Quality – Implement data quality checks, monitoring, and lineage using tools like Great Expectations or custom frameworks; enforce data governance policies (GDPR/CCPA).
· Collaboration – Partner with data analysts, product managers, and engineers to translate business requirements into technical data solutions.
· CI/CD & Automation – Automate deployment of data pipelines using Azure DevOps or GitHub Actions; maintain infrastructure as code (Terraform) for data resources.
Required Skills & Experience
· Total Experience: 10+ years in data engineering or related roles.
· Cloud Data Platforms: Deep hands on experience with Databricks (notebooks, jobs, clusters, Delta Lake, Unity Catalog) – must have production level work.
· Data Warehousing: Proven experience with cloud data warehouses (Snowflake, Azure Synapse, or Redshift) – design, optimisation, and administration.
· Data Modeling: Strong knowledge of dimensional modeling (Kimball/Inmon), relational database design, and experience with tools like ER/Studio or dbt.
· Programming: Expert in Python and SQL – ability to write maintainable, production grade code.
· Big Data: Hands on with Apache Spark (PySpark), distributed computing, and performance tuning.
· Orchestration: Experience with workflow tools (Airflow, Azure Data Factory, or Prefect) for scheduling and monitoring pipelines.
· Version Control: Proficient with Git and collaborative development workflows.
Numbers & Facts
| Location | Chicago, IL |
| Industry | Computer/IT Services |
| Company Size | 500 to 999 employees |
| Year Founded | 1990 |
| Website | http://q1tech.com/ |
About Company
Q1 consists of experienced and recognized experts providing the capability to respond to market demand in order to provide professional services for our clients including Enterprise software implementations, application integration and technical / functional support.
Q1 has steadily grown into a Quality IT services and solutions organization with the average experience of our team being over 10 years. We have continuously met or exceeded client expectations by delivering professional services and project implementations on time and under budget to help clients truly recognize return on investment.
Skills
- Apache Sparkunmatched
- Application Programming Interface (API)unmatched
- Automationunmatched
- Big Dataunmatched
- Channel Strategiesunmatched
- Cisco Unityunmatched
- Cloud Computingunmatched
- Continuous Deployment/Deliveryunmatched
- Continuous Integrationunmatched
- Data Analysisunmatched
- Data Managementunmatched
- Data Modelingunmatched
- Data Partitioningunmatched
- Data Qualityunmatched
- Data Warehousingunmatched
- Database Designunmatched
- Database Extract Transform and Load (ETL)unmatched
- DevOpsunmatched
- Dimensional Modelingunmatched
- Distributed Computingunmatched
- Embarcadero ER/Studiounmatched
- Gitunmatched
- GitHubunmatched
- Microsoft Windows Azureunmatched
- Operational Auditunmatched
- Performance Analysisunmatched
- Performance Tuning/Optimizationunmatched
- Product Engineeringunmatched
- Python Programming/Scripting Languageunmatched
- Quality Monitoringunmatched
- Relational Databases (RDBMS)unmatched
- Requirements Managementunmatched
- SQL (Structured Query Language)unmatched
- Snowflake Schemaunmatched
- Source Code/Configuration Management (SCM)unmatched
- Star Schemaunmatched
- Structured Dataunmatched
- Team Playerunmatched
- Use Casesunmatched
- Writing Skillsunmatched
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