Jobs in the United States

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Added 2 mins ago
Sr. Analyst, Supply Chain Data Analytics
United States
Engineer and maintain data foundations, reusable data models, metric views, and analytics-ready datasets using Databricks, SQL, Python, and semantic assets that support analytics, AI, and…
Added 5 mins ago
Senior Data Engineer (Remote Opportunity)
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Added 11 mins ago
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Added 13 mins ago
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CA / Bellevue, WA · Livingston, NJ · Manhattan, NY · San Francisco, CA · Sunnyvale, CA
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Added 32 mins ago
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Added 45 mins ago
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Added 51 mins ago
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Added 53 mins ago
REMOTE AI/ML Engineer II
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Added 1 hour ago
1000000567.DATA ENGINEER LEAD.INFO TECH SERVICES
Dallas County
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Provides technical and strategic leadership for the County’s data engineering function, overseeing the design, development, and delivery of scalable data systems and services. Defines…
Added 1 hour ago
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Senior AI Engineer (Agentic Systems)
Pantheon Data
Reston, VA
- $120,000 to $220,000 USD / year
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Added 1 hour ago
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Data Engineer (AI-ML Data Systems)
Pantheon Data
Reston, VA
- $110,000 to $190,000 USD / year
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Added 1 hour ago
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AbbVie

Senior Data and Analytics Engineer

North Chicago, IL, United States

$109,500 to $208,500 USD / year

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Job details

Pay: $109,500 to $208,500 USD / year
Posting language: English
Posted: Nov 2, 2026

Original job description

Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.

Job Description

The Senior Data and Analytics Engineer will be responsible for translating complex business and data requirements into scalable, secure, and high-performing analytics and data engineering solutions for the Operations (Supply Chain) organization. The role will partner closely with internal DATA (Data, AI, Transformation and Automation) teams, and cross functional Business Technology Solutions (BTS), business stakeholders, architects, and delivery teams to understand strategic priorities and design, build, and deliver modern data solutions that enables analytics, business intelligence, and AI use cases. This role requires a strong combination of hands-on technical expertise and strategic thinking, understanding the North Star platform architecture to implement practical engineering solutions while ensuring alignment with enterprise architecture, development standards, data governance, security, and engineering best practices. The individual will also provide technical leadership throughout the solution lifecycle, proactively manage technical risks and dependencies, and communicate effectively with both technical and business stakeholders.

Responsibilities

Hands-on engineering and solution delivery: Design, develop, test, deploy, and support production-grade data pipelines and data products for structured, semi-structured, and unstructured data. Build for both batch and near-real-time/streaming patterns, with reusable components, parameterized frameworks, and automated recovery.
Lakehouse and modern data warehouse architecture: Implement solutions using Amazon S3, Apache Iceberg/open table formats, AWS Glue Data Catalog, Amazon EMR/Spark, Amazon Redshift and Redshift Spectrum. Apply medallion patterns (Bronze, Silver, Gold), dimensional and domain-oriented modeling, workload isolation, partitioning, clustering/sort strategies, and performance optimization.
Data product and data mesh enablement: Translate business domains into discoverable, interoperable, and governed data products. Define product contracts, ownership, metadata, quality expectations, service-level objectives, lineage, and approved consumption patterns for dashboards, self-service analytics, APIs, data science, and AI.
Ingestion and orchestration: Engineer reliable ingestion from enterprise applications, databases, files, APIs, and event streams. Develop orchestration using tools such as Apache Airflow, dbt, AWS Step Functions or equivalent approved platforms, with dependency management, idempotency, observability, alerting, and backfill capability.
Data quality, governance, and security: Embed automated data-quality controls, reconciliation, schema validation, lineage, cataloging, retention, encryption, least-privilege access, and auditability. Partner with data governance, security, privacy, architecture, and platform teams to meet AbbVie standards and regulated-industry expectations.
DevSecOps and engineering excellence: Establish source-control, code-review, automated testing, CI/CD, infrastructure-as-code, environment promotion, release management, and operational monitoring practices. Improve reliability, cost, performance, and developer productivity through measurable engineering standards.
Business partnership and technical leadership: Understands organization's vision, goals, and strategies and partner with DATA, BTS, Operations stakeholders to translate complex requirements into scalable designs. Provide technical direction, resolve design and delivery issues, communicate risks and trade-offs, and contribute to portfolio prioritization and roadmap decisions.
Operational ownership: Own solutions through the full lifecycle, including production readiness, deployment, support transition, incident resolution, root-cause analysis, technical debt reduction, and continuous improvement. Work with minimal oversight while escalating material risks early.

Qualifications

Required:

Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, or a related field plus 7 years of relevant experience; Master's Degree plus 6 years of experience; PhD plus 2 years of experience.
Respective years of hands-on experience designing and delivering enterprise modern data warehouse, Lakehouse, data engineering, and analytical solutions.
Advanced proficiency in SQL and Python, with strong hands-on experience in PySpark or Scala and distributed data processing on Apache Spark/EMR or a comparable platform.
Demonstrated experience building large Lakehouse and modern warehouse solutions using Amazon S3, Apache Iceberg or comparable open table formats, AWS Glue, Amazon EMR, Amazon Redshift, and Redshift Spectrum.
Proven experience with medallion architecture, data modeling, schema design, incremental processing, change data capture, partitioning, performance tuning, and cost optimization.
Experience developing production-grade batch and streaming data pipelines, including orchestration, retries, backfills, observability, alerting, and operational support.
Hands-on experience with data transformation and orchestration tools such as Apache Airflow, AWS Step Functions or approved equivalents.
Experience implementing CI/CD, automated testing, Git-based development, infrastructure-as-code, and Agile DevOps delivery practices.
Demonstrated ability to build governed, AI-ready data products that support BI, self-service analytics, data science, machine learning, and contextual AI consumption.
Strong analytical, problem-solving, communication, and stakeholder-management skills, with the ability to work effectively across technical and business teams in a matrixed organization.
Experience with data catalogs, lineage, and governance platforms such as Alation, AWS Glue Data Catalog, or approved equivalents.

Preferred:

Supply Chain or Operations domain knowledge across planning, sourcing, inventory, manufacturing, logistics, or distribution.
Experience with streaming technologies such as Amazon Kinesis, Apache Kafka, Spark Structured Streaming, or equivalent platforms.
Working experience with AWS Lake Formation, IAM, Secrets Manager, CloudWatch, CloudTrail, and enterprise controls for secure data platforms.
Experience exposing data through APIs, semantic models, governed data services, or feature/context layers for AI-enabled applications.
Relevant cloud or data engineering certifications are beneficial but not required.
5+ years of hands-on experience designing and delivering enterprise modern data warehouse, Lakehouse, data engineering, and analytical solutions.

Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:

The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.
We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
This job is eligible to participate in our long-term incentive programs.

Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: https://www.abbvie.com/join-us/reasonable-accommodations.html

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More about this job

Skills

Data engineering
Supply chain management
Business intelligence
Leadership
Unstructured data
Data warehouse
AWS
EMR
Apache Spark
Amazon Redshift
Apache Airflow
dbt
Cataloging
Test automation
CI/CD
Infrastructure as code
Monitor operations in the leather industry
Prioritization
Root cause analysis
Process improvement
Computer science
SQL
Python
PySpark
Scala
Process data
Data modelling
Git
Agile
DevOps
Machine learning
Problem solving
Communication skills
Stakeholder management
Logistics
Amazon Kinesis
Apache Kafka
Identity and access management
Compensation and benefits

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