Data Engineer

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts.
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation.
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models.
• Develop and optimize Snowflake platform capabilities, including: Snowpipe, Streams, Tasks, File Formats, External Stages Dynamic Tables and ELT pipeline automation Warehouse sizing, performance tuning, and cost optimization.
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices.
• Translate complex business requirements into technical design specifications and actionable development plans.
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines.
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases.
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions. Implement robust monitoring frameworks using tools such as Snowflake Resource Monitors, CloudWatch, Datadog, or equivalent observability platforms.
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations.

Senior Data Engineer

• Build our data and AI foundation: Design how operational and financial data is captured, structured, processed, stored, and queried across Renterra. This includes event architecture, data pipelines, and analytical systems such as ClickHouse.
• Create AI-powered products: Build agents and intelligent workflows that use Renterra’s data to identify problems, answer questions, support decisions, and automate work for our customers.
• Own production quality: Make data and AI systems reliable, observable, secure, and cost-effective. Design for failure recovery, data quality, permissions, model uncertainty, and appropriate human review.
• Measure what works: Develop evaluations and feedback loops for accuracy, reliability, latency, cost, and customer impact. Use what you learn to improve both the underlying data systems and the product experience.
• Work across the company: Partner with leadership, product, engineering, support, and customers to find valuable problems and turn them into shipped solutions.
• Make pragmatic technical decisions: Choose the right approach for each problem, whether that is a deterministic workflow, analytical query, machine-learning model, LLM, vendor, or a combination of them.
• Report directly to the CTO.

Senior Software Engineer, AI & Data

• Collaborate with product managers, data scientists, engineering, and program management teams to define product features, business deliverables, and strategies for data products
• Collaborate with business partners, operations, senior management, etc on day-to-day operational support
• Support operational reporting, self-service data engineering efforts, production data pipelines, and business intelligence suite
• Interface with multiple diverse stakeholders and gather/understand business requirements, assess feasibility and impact, and deliver on time with high quality
• Design appropriate solutions and recommend alternative approaches when necessary
• Research, prototype, and recommend emerging tools and technologies across cloud data engineering and Generative AI
• Enable current Generative AI initiatives through the development of pipelines that process unstructured data, transform text, integrate models, and validate generated outputs
• Work with high volumes of data, fine-tuning database queries and able to solve complex technical problems
• Contribute to multiple projects/demands simultaneously
• Work in a fast-paced, collaborative, and iterative environment
• Exercise independent judgment in methods and techniques for obtaining results
• Work in an agile/scrum environment
• Use state-of-the-art technologies to acquire, ingest and transform big datasets

• Leads the architecture, engineering, and ongoing operation of the security data lake and associated data products supporting multiple security technologies and data sources
• Establishes the technical roadmap, engineering standards, reusable design patterns, and platform operating model
• Acts as a subject matter expert among peers, with manager and senior management on all matters pertaining to data platforms and the supporting architecture
• Develop and maintain the end-to-end architecture of the security data lake, including ingestion, storage, transformation, enrichment, semantic, and consumption layers
• Ensure data pipelines comply with security policies and regulatory requirements
• Develop testing plans to ensure quality of implementation
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues
• Maintain relationship with key vendors
• Create presentations and seek IT management approval and acceptance of significant replacements or reconfigurations of the data pipeline

• Design and manage Feed the Children’s modern cloud data infrastructure, with ownership of Microsoft Fabric and Azure.
• Lead the stand up and evolution of Feed the Children’s Fabric platform and serve as its primary Product Owner.
• Collaborate closely with ERP and CRM teams, and coordinate with Data Governance leadership.
• Ensure seamless integration of data and data flows from various sources, including ERP systems, CRM systems, DHIS2, external datasets and APIs, and other business applications.
• Implement processes and tools to ensure high data quality and consistency across the organization.

Data Architect

• Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment.
• Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications.
• Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
• Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
• Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
• Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Data Scientist – Signal Processing Engineer, Acoustics

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases
• Develop data transformations using Delta Lake and Lakehouse architecture best practices
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance
• Participate in migrating and modernizing legacy data processes to cloud-native architectures
• Improve data quality through validation, testing, monitoring, and automation
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification

Data Engineer II

• Designing and implementing AI systems to transform raw data into actionable insights
• Developing scalable machine learning models to support client decision-making processes
• Collaborating with cross-functional teams to integrate AI solutions into existing platforms
• Conducting data wrangling and preprocessing to prepare datasets for analysis
• Applying advanced algorithms to enhance the performance of AI models
• Building and deploying software systems that leverage AI and machine learning technologies
• Analyzing complex data sets to identify patterns and trends that inform business strategies
• Supporting client engagements by delivering quality work and adapting to diverse project requirements
• Engaging in continuous learning to deepen technical skills and knowledge in AI engineering
• Upholding professional and technical standards while contributing to team success

AI Engineer, AI Experienced Associate

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand, improve service SLAs, and minimize gaps
• Serve as the escalation point for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health and streamline tasks
• Monitor backup systems and performance, identifying and troubleshooting issues
• Configure access using a least-privileged approach, audit, and ensure adherence to policies
• Communicate upgrade and rollback plans, and execute upgrades
• Build lasting trust with clients

Data Protection Engineer

• Develops, documents, tests, debugs, and maintains data; provides expertise and familiarity in software tools, frameworks and best practices in secure software engineering.
• Collaborates effectively with other members of the team to deliver on the team’s backlog.
• Participates in the discussion and implementation around complex capabilities.
• Develops complex features and capabilities to enhance the Early Warning core product offerings.
• Manages source code using Git.
• Receives, reviews and approves pull requests ensuring that code quality remains high.
• Processes both real time stream data and file based data through ETL frameworks and persist the data into the data store.
• Builds aggregations on the data to enable provisioning of data to the AI/ML models as input and also deliver them to the customers.
• Document complex details and decisions in Confluence.
• Builds automated tests alongside sprint work that is integrated into the CI/CD pipeline, considering environment variables, and provides recommendations.
• Able to troubleshoot complex bugs that may arise during development.
• Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

Senior Software Engineer – Data Engineering

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand
• Serve as the escalation path for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health, backup and recovery jobs, and general environment status
• Monitor backup systems and performance, identify issues needing attention

• Building and leading a team to deliver technology products and services that meet business outcomes.
• Developing a technology strategy, ensuring technology solutions comply with applicable standards.
• Promoting design, engineering, and organizational practices.
• Advocating and advancing modern, Agile solution delivery practices.
• Coaching, mentoring, providing feedback and hands-on career development.
• Identifying emerging talent and fostering leadership skills.
• Managing stakeholders and relationships with business and technology partners.
• Contributing to the technology strategy for their technical domain.
• Creating an inclusive and healthy working environment.

Senior Technical Manager – Enterprise Data Platform

• Build and maintain curated data layers and data sources
• Develop and optimize SQL- and Python-based transformations in Snowflake
• Troubleshoot data quality issues originating from upstream pipelines
• Validate incoming datasets and ensure accuracy, completeness, and consistency
• Design and manage analytics-ready data models and tables to support reporting and dashboards
• Serve as a key point of contact for reporting data layers

Data Engineer, Data Operations

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Design and build scalable data ingestion pipelines from structured and unstructured data sources into Snowflake.
• Develop and maintain ELT/ETL processes to transform, cleanse, and integrate enterprise data.
• Design reusable dimensional, semantic, and business-ready data models that support analytics and AI use cases.
• Build and maintain consumable data products including curated datasets, data marts, semantic layers, APIs, and AI-ready data assets.
• Design and implement enterprise data architectures that support scalability, interoperability, and future AI adoption.
• Design and curate AI-ready datasets that support machine learning, generative AI, intelligent agents, and advanced analytics.
• Implement metadata, lineage, and semantic modeling capabilities that improve data discoverability and AI readiness.
• Collaborate with AI and analytics teams to establish patterns for retrieval, search, knowledge management, and AI-enabled business solutions.
• Evaluate and adopt emerging Snowflake capabilities, including Cortex AI, semantic models, vectorized data structures, and AI-related platform services.
• Apply Data Product Management principles by establishing ownership, quality standards, service levels, and lifecycle management processes.
• Implement data quality monitoring, automated validation, observability, and governance frameworks.
• Ensure compliance with enterprise security, privacy, regulatory, and data governance requirements.
• Establish and maintain metadata standards, data lineage, business definitions, and cataloging practices.
• Optimize Snowflake performance through query tuning, workload management, storage optimization, and architectural improvements.
• Ensure high availability, reliability, and scalability across data platforms and pipelines.
• Implementing proactive monitoring, alerting, and observability practices.
• Drive cloud and Snowflake cost optimization through consumption monitoring, capacity planning, and engineering best practices.
• Lead and mentor a team of 3–4 data engineers, providing technical leadership, coaching, and career development.
• Serve as the technical subject matter expert for Snowflake, modern data platforms, and AI-ready data engineering practices.
• Define and enforce enterprise data engineering standards, architectural patterns, and development best practices.
• Collaborate with business stakeholders, product owners, analysts, architects, and data scientists to translate business objectives into scalable data solutions.

Lead Data Engineer, Snowflake

• Shape Nasdaq’s Index Platform by leading the onboarding, standardizing, and transforming vendor and internal data
• Define business requirements and data methodologies for global indexes
• Lead ideation sessions for platform features and enhancements
• Own the platform roadmap and delivery lifecycle from ideation to production
• Act as a primary point of contact for internal stakeholders
• Define and analyze key metrics to measure product performance
• Drive continuous improvement through data-driven insights
• Collaborate across Nasdaq groups for best practices and data sharing

Senior Director – Platform Manager – Index Data

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Lead strategy, prioritization, and roadmap alignment across multiple internal platform capability areas
• Own the strategy, roadmap, and evolution of internal data platform capabilities, including data ingestion, cross-cutting data services, the Enterprise Data Layer (EDL), data quality, metadata management, governance capabilities, and source onboarding
• Partner with Vizient Intelligence engineering, AI Delivery, architecture, security, and product leadership to define platform investment priorities
• Establish decision frameworks that sequence platform capabilities, address technical debt, improve platform reuse, enhance internal developer productivity, and support AI-assisted and agentic development workflows
• Align platform roadmaps with measurable outcomes, including reduced delivery friction, increased platform adoption, stronger governance, faster product delivery, and improved AI-first delivery maturity
• Drive platform capabilities that improve the reliability, accessibility, and reuse of enterprise data across Vizient Intelligence products
• Collaborate closely with data engineering teams to define platform capabilities that support scalable, high-quality, and governed data solutions
• Facilitate executive stakeholder alignment on platform direction, funding, dependencies, tradeoffs, adoption expectations, and AI-first operating model changes
• Represent platform product priorities in leadership forums by translating technical platform strategies into business-relevant decisions and resolving complex cross-functional priorities
• Guide and develop a small team of platform product professionals through coaching, mentorship, and professional development
• Establish lightweight platform product management standards, including discovery practices, roadmap narratives, success measures, prompt and context patterns, release planning, adoption planning, and responsible AI tool usage
• Build strong partnerships across engineering, AI Delivery, architecture, security, design, and internal product teams to enable consistent platform adoption and delivery outcomes

Lead Director, Platform Product Management – Data Platform & Operations

• Lead the strategy and execution of complex and large Data and Analytics portfolio.
• Develop and implement a strategic roadmap to modernize legacy data and analytics ecosystems using Cloud and AI.
• Ensure data architecture and solutions align with enterprise-wide standards for Data, AI and Analytics.
• AI Data Engineering leader responsible for Implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions.
• Design, build and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• Oversee the design, development, and maintenance of lakehouse architectures, data pipelines, data warehouses, data lakes and reporting systems.
• Mentor and lead a high-performing team including Directors, Business data analysts, Data engineers and Release train engineers.
• Identify and champion developer productivity improvements across the end-to-end data management lifecycle.

Data Engineer jobs in Illinois

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Senior Data Engineer

Senior Software Engineer, AI & Data

Senior Data Engineer

Senior Data Engineer

Data Architect

Data Scientist – Signal Processing Engineer, Acoustics

Senior Data Engineer

Data Engineer II

AI Engineer, AI Experienced Associate

Data Protection Engineer

Senior Software Engineer – Data Engineering

Data Protection Engineer

Senior Technical Manager – Enterprise Data Platform

Data Engineer, Data Operations

Senior Software Engineer, Data

Lead Data Engineer, Snowflake

Senior Director – Platform Manager – Index Data

Senior Software Engineer, ERP, Data & Domain

Lead Director, Platform Product Management – Data Platform & Operations

Director, Data Architect