Data Engineer

Javen Technologies

Chicago, IL
30+ days ago

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Skills

Access Controlunmatched
Agile Programming Methodologiesunmatched
Amazon Web Services (AWS)unmatched
Apache Sparkunmatched
Application Programming Interface (API)unmatched
Banking Servicesunmatched
Business Intelligenceunmatched
Cisco Unityunmatched
Cloud Computingunmatched
Continuous Deployment/Deliveryunmatched
Continuous Integrationunmatched
Data Managementunmatched
Data Modelingunmatched
Data Processingunmatched
Data Qualityunmatched
Data Scienceunmatched
Data Storageunmatched
Database Extract Transform and Load (ETL)unmatched
Financial Servicesunmatched
High Availabilityunmatched
Identify Issuesunmatched
Identity Data Managementunmatched
Information/Data Security (InfoSec)unmatched
Machine Learningunmatched
Maintain Complianceunmatched
Metadata Identificationunmatched
Microsoft Windows Azureunmatched
Operational Supportunmatched
Operations Processesunmatched
Privacy Controlsunmatched
Production Supportunmatched
Query Optimizationunmatched
Reconciliationunmatched
Regulatory Complianceunmatched
Regulatory Reportsunmatched
Requirements Managementunmatched
Riskunmatched
Root Cause Analysisunmatched
SQL (Structured Query Language)unmatched
Service Level Agreement (SLA)unmatched
Structured Dataunmatched
Tableauunmatched
Unstructured Dataunmatched

Description

Job Title: Data Engineer - (Databricks/ Spark/ Delta Lake)
Location: Chicago, IL (onsite – Only Locals Required)
Duration: 6 Months contract+

Job Description:
Key Responsibilities:
Data Engineering & Pipeline Development

Design, develop, and maintain end-to-end data pipelines in Databricks using Spark and Delta Lake
Build and optimize ELT/ETL processes for structured and unstructured data ingestion into the Data Lakehouse
Implement scalable ingestion patterns (batch and event-driven) from internal systems, third-party APIs, and cloud sources
Develop data models (bronze, silver, gold layers) to support enterprise reporting, analytics, and downstream consumption

Data Platform & Integration

Integrate the Data Lakehouse with enterprise tools such as Tableau, Alteryx, and machine learning platforms
Design and implement data access controls, identity management, and secure data sharing mechanisms
Support API-based integrations and downstream data consumption patterns

Data Quality, Governance & Controls

Implement data quality checks, reconciliation processes, and monitoring within Databricks pipelines

Ensure adherence to enterprise data governance standards, including lineage, metadata, and audit requirements
Support regulatory and compliance requirements (e.g., data integrity, privacy, and security controls)

Cloud & Automation

Develop and manage workflows using orchestration tools (e.g., Airflow, Control-M)
Automate data pipelines, deployments, and operational processes through CI/CD pipelines
Leverage cloud-native services (AWS/Azure) for data processing, storage, and event-driven architectures
Operations & Support Monitor, troubleshoot, and optimize data pipelines and Spark workloads for performance and reliability
Support production data platforms, including incident resolution and root cause analysis
Ensure high availability, data integrity, and SLA adherence across enterprise data systems

Collaboration

Partner with data architects, data scientists, BI teams, and business stakeholders to deliver data solutions

Participate in Agile ceremonies and contribute to iterative delivery of data products
Translate business requirements into scalable technical data solutions

Required Qualifications:

3+ years of experience in data engineering, data platforms, or related roles
Hands-on experience with Databricks, Apache Spark (PySpark), and Delta Lake
Strong SQL and data modeling skills (relational and dimensional)
Experience building and supporting data pipelines in a cloud environment (AWS or Azure)
Experience with ELT/ETL tools (e.g., Fivetran, custom ingestion frameworks)
Familiarity with data orchestration tools (Airflow, Control-M)
Experience working in Agile development environments

Preferred Qualifications:

Experience in financial services or regulated environments (e.g., banking, risk, regulatory reporting)
Knowledge of data governance frameworks and tools (e.g., Collibra)
Experience with real-time or streaming data pipelines
Exposure to machine learning pipelines and feature engineering in Databricks
Cloud certifications (AWS, Azure, or Databricks)

Technical Skills:

Databricks (Lakehouse architecture, notebooks, jobs, Unity Catalog)
Spark / PySpark
SQL (advanced querying and optimization)

Numbers & Facts

Location

Chicago, IL

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