below marketmost similar roles pay hereabove market
This role pays less than
59%
of similar roles. Most pay
$140,971–$205,781
— the blue band above.
At the midpoint, this role pays about
$159k
versus about
$173k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
130 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 122 roles with salary data.
The Senior Data and Analytics Engineer joins the Operations Supply Chain organization to translate complex business requirements into scalable, secure data engineering solutions. This role involves designing, building, and supporting production-grade data pipelines for structured and unstructured data, supporting both batch and streaming patterns. Key responsibilities include implementing Lakehouse and modern data warehouse architectures, managing data products within a data mesh, and ensuring data quality, governance, and security. The engineer will utilize a technical stack including SQL, Python, PySpark, Scala, Amazon S3, Apache Iceberg, AWS Glue, Amazon EMR, and Amazon Redshift. Core tasks involve orchestration using Apache Airflow and dbt, alongside DevSecOps practices like CI/CD and infrastructure-as-code. The position solves technical challenges in supply chain operations by creating governed, AI-ready data products for analytics and machine learning.
Design, develop, and support production-grade data pipelines for batch and near-real-time streaming patterns.
Implement Lakehouse and modern data warehouse architectures using Amazon S3, Apache Iceberg, and Amazon Redshift.
Translate business requirements into governed data products with defined contracts, metadata, and quality expectations.
Engineer reliable data ingestion and orchestration using tools like Apache Airflow, dbt, and AWS Step Functions.
Embed automated data-quality controls, security protocols, and lineage tracking into all data solutions.
Establish DevSecOps practices including CI/CD, infrastructure-as-code, and automated testing for engineering excellence.
Provide technical leadership by resolving design issues and communicating risks to business stakeholders.
Own the full solution lifecycle including production readiness, incident resolution, and root-cause analysis.
What we're looking for
Bachelor’s Degree in Computer Science, Information Systems, Data Engineering, or related field plus 7 years experience; Master's plus 6 years; PhD plus 2 years.
Experience designing and delivering enterprise modern data warehouse, Lakehouse, data engineering, and analytical solutions.
Advanced proficiency in SQL and Python with hands-on experience in PySpark or Scala and distributed data processing on Apache Spark/EMR.
Experience building Lakehouse solutions using Amazon S3, Apache Iceberg, AWS Glue, Amazon EMR, Amazon Redshift, and Redshift Spectrum.
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 with orchestration, retries, backfills, observability, alerting, and operational support.
Experience with data transformation and orchestration tools such as Apache Airflow, AWS Step Functions, or approved equivalents.
Experience with data catalogs, lineage, and governance platforms such as Alation, AWS Glue Data Catalog, or approved equivalents.
Supply Chain or Operations domain knowledge across planning, sourcing, inventory, manufacturing, logistics, or distribution (preferred).
Experience with streaming technologies such as Amazon Kinesis, Apache Kafka, Spark Structured Streaming, or equivalent platforms (preferred).
Working experience with AWS Lake Formation, IAM, Secrets Manager, CloudWatch, CloudTrail, and enterprise controls for secure data platforms (preferred).
Experience exposing data through APIs, semantic models, governed data services, or feature/context layers for AI-enabled applications (preferred).
Relevant cloud or data engineering certifications (preferred).