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Data Engineer 4 (Manager, IC)

Information Technology Senior Management Forum Chicago, IL

Chicago, IL

Today

$179k - $205k/yr Full time Backend Developer Manager Big Data

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Job Description

The Data Engineer 4 (Manager, IC) role is an onsite opportunity in Chicago, IL with the Information Technology Senior Management Forum. In this position, you will help build and deliver cloud-first data solutions, while also leading engineering work across Agile teams. The work focuses on scalable data pipelines, reusable engineering patterns, and security and compliance practices.

As a senior engineering contributor, you will work at the intersection of data engineering and distributed systems, using modern tooling and data platforms to support robust experiences for millions of Americans. The role is designed for strong technical ownership, including designing and delivering solutions with minimal support.

What you’ll do

Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies
Influence a team of developers, data analysts, and data scientists with experience spanning machine learning, distributed microservices, lakehouse architecture, and full-stack systems
Use Python and Spark with open-source relational and NoSQL databases and cloud data warehousing platforms including Databricks and Snowflake
Experiment with and learn new technologies, participate in internal and external technology communities, and mentor others in the data community
Work with product managers and software engineers to deliver robust cloud-first data solutions
Independently design, build, and deliver cloud data solutions and applications with little or no support from supervisors or managers
Architect and enforce common data engineering design patterns to improve code quality, maintainability, and reusability
Design and build data pipelines and platforms with an emphasis on scalability, resilience, and operational efficiency
Implement data security standards, including encryption at rest, encryption at transit, and fine-grained access control to meet data privacy regulations
Serve as a data engineering ambassador by communicating technical concepts and data outcomes clearly to internal and external stakeholders

Required qualifications

Bachelor’s Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 4 years of experience in application development (internship experience does not apply)
At least 2 years of experience in distributed data
At least 2 years of experience with SQL
At least 2 years of experience with one programming language: Python, Java, or Scala
At least 2 years of experience in data pipeline design and development
At least 1 year of experience in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Technologies you’ll work with

Python, Spark
Databricks, Snowflake
SQL, relational databases, NoSQL databases
Agile
Machine learning
Distributed microservices, lakehouse architecture
Encryption at rest, encryption at transit, fine-grained access control

Preferred qualifications

7+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java
4+ years of hands-on experience designing, deploying, and operating data workloads in at least one public cloud environment (AWS, Microsoft Azure, or Google Cloud)
4+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
4+ years of experience designing, implementing, and operating real-time or streaming data pipelines
2+ years of experience working on data observability (e.g., Monte Carlo, Splunk) or data orchestration tools (e.g., Airflow, Dagster)
4+ years of experience working with unstructured or semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
4+ years of experience designing and supporting data warehousing solutions (e.g., Snowflake, Redshift)
2+ years of experience working in an Agile development environment
2+ years of experience developing user-centric reusable data products

Compensation and location

Location: Chicago, IL (onsite)
Salary: USD 179,400 to 204,700 per year

Immigration authorization

Capital One will not sponsor a new applicant for employment authorization or provide immigration-related support for this position (including H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, O-1, and other work authorization forms requiring employer immigration support).

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7d ago

Data Engineer, Microsoft Fabric and AI Director

GS1 Ewing, NJ

Ewing, NJ

7d ago

$140k - $160k/yr Full time Manager Analytics Artificial Intelligence

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Job Description

GS1 Global Office is hiring a senior, hands-on Data Engineer Director (individual contributor) to build secure, scalable enterprise analytics and AI-enabled data pipelines.

Responsibilities

Design, develop, and maintain scalable data pipelines using Microsoft Fabric and Azure data services.
Build and support Lakehouse, Data Warehouse, and semantic model solutions using reusable design patterns.
Create reliable data integration processes using Microsoft Fabric Data Pipelines, Azure Data Factory, APIs, and other approved integration methods.
Support migration and modernisation initiatives involving Microsoft Fabric and Azure analytics services.
Optimise data processing for performance, scalability, maintainability, and cost efficiency.
Influence enterprise data architecture decisions and help evolve shared data models and analytics standards.
Design and build retrieval-augmented generation (RAG) pipelines that securely connect approved enterprise data to Claude AI and other approved large language model platforms.
Develop AI-enabled search and analytics building blocks, including data preparation, chunking, embeddings, indexing, and retrieval.
Support responsible adoption of approved enterprise AI and development tools, including Claude AI and Claude Code, with secure data-handling and access patterns.
Evaluate and prototype AI-enabled analytics use cases with senior leaders and technical stakeholders, including assessment of business value, architecture, risk, and guardrails.
Monitor and improve AI pipeline reliability, quality, performance, and cost in line with GS1 governance standards.
Maintain documentation, traceability, and human oversight for AI-enabled solutions.
Develop and maintain Power BI dashboards, reports, datasets, and semantic models to deliver actionable insights.
Support operational reporting, data-quality reporting, and prioritized ad hoc analytics requests.
Work with business stakeholders to understand requirements, define acceptance criteria, and translate needs into sustainable reporting solutions.
Promote consistent definitions, measures, and reporting practices across the organisation.
Implement data validation, observability, monitoring, and quality controls across data pipelines and analytics solutions.
Support metadata management, data lineage, documentation, retention, and governance requirements.
Design solutions aligned with GS1 information security, data privacy, access-control, and responsible AI requirements.
Implement role-based access controls, data classification, and auditability for data sensitivity and intended use.
Identify and escalate concerns related to data quality, security, privacy, model risk, and governance.
Develop automated testing and validation for data pipelines, semantic models, reports, and AI-enabled solutions.
Use Git, source control, CI/CD, and environment-management practices for reliable deployments across development, test, and production.
Monitor critical solutions, resolve incidents, and troubleshoot failures including refresh errors, reporting issues, RAG errors, and performance bottlenecks.
Perform root-cause analysis and implement preventative improvements.
Maintain technical documentation, operational runbooks, and recovery procedures; support release validation and business-continuity activities.
Provide technical leadership on data architecture, solution design, engineering standards, and responsible AI implementation.
Review code and solution designs, share knowledge, and coach team members in data engineering and analytics practices.
Partner with Product Owners, Data Engineers, QA, Software Engineering, and business stakeholders across a globally distributed organisation.
Communicate technical options, dependencies, risks, and costs to technical and non-technical audiences.
Assess trade-offs and make recommendations balancing business value, usability, security, scalability, cost, and maintainability.
Contribute to planning, architecture discussions, continuous improvement, and workload priorities across the BIDA team.

Requirements

Bachelor’s degree in computer science, data engineering, information systems, or a related field (or equivalent relevant professional experience).
At least 5 years of relevant experience in data engineering, business intelligence, or analytics, including responsibility for production solutions.
Strong practical experience with Microsoft Fabric, or significant experience with Azure Synapse, Databricks, or comparable modern cloud analytics platforms.
Strong experience developing Power BI reports, semantic models, and datasets.
Advanced SQL skills and experience with Azure SQL or comparable relational database services.
Practical experience building ETL or ELT solutions using Azure Data Factory, Microsoft Fabric Data Pipelines, or comparable orchestration tools.
Experience with dimensional modelling, data warehousing, and enterprise semantic models, including DAX.
Proficiency with Python and/or PySpark for data processing and automation.
Practical experience building/supporting RAG pipelines and working with large language model APIs such as Claude, OpenAI, or comparable platforms.
Experience with embeddings, vector search, or vector databases, plus prompt-based retrieval patterns and structured/unstructured data integration.
Experience with source control, automated testing, CI/CD, and production monitoring.
Experience implementing data security, role-based access controls, and privacy requirements in cloud data and analytics environments.

Technologies

Microsoft Fabric
Azure data services
Power BI
Azure Data Factory
Microsoft Fabric Data Pipelines
APIs
Lakehouse
Data Warehouse
Semantic models
Azure Synapse
Databricks
DAX
SQL
Azure SQL
Git
CI/CD
Python
PySpark
Retrieval-augmented generation (RAG)
Claude AI
Claude Code
OpenAI
Embeddings
Vector search
Vector databases
Prompt-based retrieval patterns
Role-based access controls

Preferred Experience

Microsoft Fabric or related Microsoft data-platform certification.
Experience with Microsoft Purview, OneLake, Azure DevOps, or GitHub.
Experience with Power BI administration, tenant governance, or capacity management.
Experience with large-scale analytical datasets and cloud cost optimisation.
Experience with Claude AI, Claude Code, or comparable enterprise AI coding and knowledge-work tools.
Knowledge of AI governance, responsible AI, model evaluation, and enterprise data-privacy practices.
Experience working in a global, federated, or matrixed organisation.

Travel Requirements

Occasional international travel and regular collaboration across European and United States time zones.

Location: Ewing, NJ (onsite) • Salary: USD 140,000 - 160,000 per year • Experience: 5+ years

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8d ago

Data Engineer

Sally Beauty Supply LLC Denton, TX

Denton, TX

8d ago

Full time Azure Data Engineer Big Data Bigdata

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Job Description

Sally Beauty Supply LLC offers a hybrid work setup based in Denton, TX (hybrid), with opportunities to design and scale enterprise data solutions on the Databricks Lakehouse Platform. You will collaborate with stakeholders to support reporting, analytics, AI, and machine learning initiatives, while building reliable pipelines and governance-ready data models. The role also comes with benefits that support everyday life and long-term planning, including medical, dental, vision, and 401(k) with company match.

Responsibilities

Design, develop, and maintain scalable data pipelines using the Databricks Lakehouse Platform.
Build ETL/ELT processes using Apache Spark, PySpark, Spark SQL, and Delta Lake.
Design and implement Medallion Architecture (Bronze, Silver, Gold).
Develop batch and streaming ingestion pipelines from enterprise systems, APIs, and cloud platforms.
Create reusable transformation frameworks with PySpark and SQL.
Optimize Spark jobs and Delta Lake tables for performance, scalability, and cost efficiency.
Configure and support Unity Catalog for data governance, security, and lineage.
Develop workflows using Databricks Workflows and Jobs.
Support Change Data Capture (CDC) and incremental data processing.
Build semantic data models for reporting, analytics, AI, and machine learning.
Implement automated testing, monitoring, and alerting for data pipelines.
Create technical documentation, metadata, and operational standards.
Research and recommend new technologies and platform enhancements.

Requirements

Bachelor’s degree in computer science, Information Systems, Software Engineering, Data Engineering, or a related technical field.
3+ years of experience in Data Engineering or Software Engineering.
1+ years of hands-on experience with the Databricks Lakehouse Platform.
Experience with Azure Data Lake Storage (ADLS), Amazon S3, or similar cloud storage platforms.
Experience with Microsoft Azure, AWS, or Google Cloud Platform (Azure preferred).
Experience supporting enterprise analytics platforms such as Power BI.
Retail, merchandising, supply chain, or eCommerce experience preferred.

Tech Stack

Databricks Lakehouse Platform, Apache Spark, PySpark, Spark SQL, Delta Lake, Medallion Architecture, Unity Catalog, Databricks Workflows and Jobs, Change Data Capture (CDC), SQL, Azure Data Lake Storage (ADLS), Amazon S3, Microsoft Azure, AWS, Google Cloud Platform, Power BI, Delta Live Tables (DLT), Databricks Workflows, REST API, and enterprise system integration.

Benefits

Medical
Dental
Vision
Life Insurance
Paid vacation and sick days
Paid holidays
Tuition reimbursement
401(k) with company match
In-house salon with complementary services
Varied selection of food options at the corporate campus
Sally Beauty and CosmoProf Professional onsite store merchandise discount

About the Role

This is a hybrid position working from the Legacy West Support Center in Plano, Texas.

Working Conditions & Physical Requirements

The work environment includes everyday risks or discomforts typical of offices, meeting and training rooms, retail stores, residences, or commercial vehicles, with normal safety precautions. The work area is adequately lighted, heated, and ventilated. The role is sedentary, with comfort while sitting and only occasional walking, standing, bending, carrying of light items (such as papers, files, books, or small parts), using a keyboard, and driving an automobile. No special physical demands are required.

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8d ago

Senior Data Engineer & Data Scientist – Commercial Intelligence

International Motors, LLC Lisle, IL

Lisle, IL

8d ago

$160k - $240k/yr Full time Senior Analytics Artificial Intelligence

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Job Description

International Motors, LLC is looking for a senior technical leader to support commercial intelligence for Retail Sales Management (RSM). In this hybrid role from Lisle, IL, you will build scalable data pipelines and data products that transform integrated commercial and enterprise information into intelligence embedded directly into dealer and sales workflows.

This position blends data engineering with machine learning and AI/LLM prototyping, with an emphasis on next-best-action and opportunity identification that teams can operationalize in day-to-day selling and dealer execution.

What you’ll do

Design, build, and maintain scalable data pipelines and data products that support RSM and broader commercial intelligence capabilities.
Integrate, normalize, and model data spanning customers, prospects, dealers, vehicles, sales, service, parts, CRM, digital interactions, and external sources.
Develop durable solutions for identity resolution, entity matching, deduplication, normalization, data lineage, and data quality across complex commercial datasets.
Build and operationalize analytical and machine learning models for lead scoring, opportunity identification, customer segmentation, propensity, prioritization, and next-best-action recommendations.
Convert complex commercial data into actionable intelligence embedded into dealer and sales workflows, not limited to dashboards or reports.
Collaborate with Product, Sales, Commercial, UX, Data, and Engineering teams to turn high-value business needs into production-ready data and analytical solutions.
Create reusable commercial data models, semantic structures, and ontology concepts covering customers, prospects, vehicles, dealers, opportunities, and sales activity.
Define and track data quality, model performance, and business-impact metrics, and resolve issues that affect trust in commercial intelligence.
Prototype and evaluate AI/ML approaches, including generative AI and LLM-based capabilities, to improve commercial decision-making and sales effectiveness.
Establish engineering and data-science practices including testing, observability, documentation, governance, versioning, CI/CD, and production deployment.
Own the full lifecycle of what you build, including operational readiness, production support, incident response, quality standards, and ongoing technical management.
Act as a senior technical leader in the RSM product team, influencing architecture and technical direction while mentoring other practitioners.
Communicate complex findings and technical decisions clearly to technical teams, product leaders, commercial stakeholders, and senior leadership.

Minimum qualifications

5+ years of data engineering, data quality, and/or analytics or statistical analysis experience (with a Master’s degree).
Strong hands-on experience building production-grade data pipelines, data models, analytical products, and commercial intelligence solutions at enterprise scale.
Advanced SQL and Python skills with sound software engineering practices.
Experience with modern cloud data platforms and distributed processing, with Palantir Foundry/AIP and/or Databricks strongly preferred.
Experience developing, deploying, and monitoring machine learning or statistical models on large, complex datasets.
Strong understanding of data quality, lineage, observability, governance, testing, and production operations.
Experience with entity resolution, identity matching, customer-360, master data, or similar commercial data problems.
Experience with customer, sales, CRM, marketing, vehicle, transactional, service, parts, or other commercial datasets.
Ability to translate ambiguous commercial problems into data products, models, experiments, and measurable outcomes.
Experience embedding analytical intelligence into operational applications and workflows.
Familiarity with LLMs, retrieval, AI agents, or AI-assisted decision systems.
Demonstrated ability to influence technical direction and drive outcomes across Product, Engineering, Data, and business teams.
Highly proactive, ownership-driven mindset capable of operating independently in ambiguity.
Excellent written and verbal communication skills.
Coach and mentor other data scientists and data engineers.
Legally authorized to work in the United States on an unrestricted basis (US Citizen, Legal Permanent Resident, Refugee or Asylee). Employment sponsorship is not anticipated (e.g., H-1B).

Preferred technologies and skills

SQL, Python, Palantir Foundry, Palantir AIP, Databricks
Machine learning, LLMs, generative AI, LLM-based capabilities, retrieval, AI agents
CI/CD, API-first and event-driven architectures
Ontology, ontology concepts, semantic modeling, ontology design
OSDK (and related Palantir Foundry experience, including pipelines, ontology design, AIP, and operational applications)

Compensation and location

Location: Lisle, IL (hybrid)
Salary: USD 160,000 - 240,000 per year

Benefits

Competitive market-based compensation.
Comprehensive benefits package designed to support employee wellbeing.

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9d ago

Staff Data Engineer

LiveView Technologies Seattle, WA

Seattle, WA

9d ago

$172k - $221k/yr Full time Artificial Intelligence Big Data Data Architecture

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Job Description

LiveView Technologies is hiring a Staff Data Engineer in Seattle, WA (onsite) to lead the end-to-end data flywheel behind physical AI. In this Senior IC/technical leadership role, you will convert raw edge telemetry and video into labeled training data, frozen evaluation and benchmark sets, and governed dataset outputs that support model training and evaluation.

Responsibilities

Own the end-to-end loop that transforms raw edge telemetry and video into labeled training data and frozen evaluation sets, and then feeds model outputs back into the next iteration.
Build and maintain pipelines that register raw source data, standardize it into a single well-defined schema, and join and aggregate it into curated datasets, so teams train, validate, and benchmark from a consistent store through a single reader rather than duplicating data transformations per use case.
Own how labels and semantic annotations are appended to datasets without rewriting source data, including versioning, quality checks, and serving; partner with annotation and data-operations teams on label production and verification while you own dataset, storage, and serving.
Own frozen, versioned validation and benchmark datasets that preserve comparability over time, including scrubbing and review discipline before sets are shared externally.
Set schema and content versioning practices that allow producers to evolve datasets without breaking consumers, using opt-in versions, append-without-rewrite for new fields, and reader/writer indirection for controlled data migration instead of forced lockstep changes.
Provide the read/write libraries and integrations researchers rely on, including support for PyTorch/Lightning dataloaders, a record-level CRUDL API, and Spark and analytics access with self-service capabilities for AI teams.
Implement governance within the flywheel so classification, scrubbing and anonymization during load jobs, and lineage and provenance for each dataset version, annotation campaign, and training input are enforced by machine rather than handled after the fact.
Define data-engineering standards for flywheel schema conventions, dataset contracts, and quality gates, and mentor engineers toward those standards as the function expands.

Requirements

8+ years building and operating production large-scale data pipelines and data-lake or lakehouse systems, including ingestion, ETL/ELT, partitioning, storage-format decisions, and reader/writer libraries used by downstream consumers.
Experience building pipelines for model training and evaluation, labeled data, and evaluation/benchmark sets, with a working understanding of how data quality and versioning influence model results.
Strong background with medallion-style layered data architectures and modern table or lake formats such as Iceberg, Delta, Parquet, or comparable systems, including schema evolution and dataset versioning.
Hands-on experience with large multimodal datasets including video, images, and sensor or telemetry, with knowledge of storage and access patterns required for queryable scale (such as denesting, repartitioning, and binary-inline versus reference storage).
Practical familiarity with the data layer of ML frameworks, including PyTorch/Lightning dataloaders and Spark, along with strong Python skills.
Experience enforcing data governance in pipelines covering classification, access control, lineage and provenance, and retention, with attention to privacy-sensitive data.
Track record of setting data-engineering direction and leveling up other engineers, with technical leadership; formal management is not required.
Bachelor’s or Master’s in Computer Science, Engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

Streaming or near-real-time ingestion from edge/IoT sources into a data lake (for example, Kafka, Lambda, EMR, or similar).
Append-without-rewrite and hash-indexed dataset techniques on open table formats, plus dataset and feature-versioning systems.
Generative-AI data work such as fine-tuning and evaluation dataset curation for LLMs and VLMs.
Experience exposing datasets to AI agents through MCP-style query interfaces, including semantic schema and plain-language documentation for retrieval.
Experience with computer-vision and video annotation tooling and workflows (for example, Encord and Labelbox, or similar).

Required Technologies

PyTorch, Lightning, Spark, Python
Iceberg, Delta, Parquet
Kafka, EMR, Lambda
MCP
PyTorch/Lightning dataloaders, Encord, Labelbox
CRUDL API

Compensation

The beginning annual salary range for this role is $171,900 - $221,000 USD. The final offer is determined by location, job-related experience, and education or training.

Total earning potential is increased through a bonus structure tied to goals, and you will become an owner from day one through LiveView Technologies’ employee equity program.

Benefits

Comprehensive health, dental and vision coverage
Retirement benefits (401k match up to 4%)
Flexible PTO

Location

Seattle, WA (onsite)

Compensation Context

This role is compensated with a starting salary range of $171,900 - $221,000 USD, determined by location, job-related experience, and education or training.

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