Senior Data and Analytics Engineer
About The Position
The Senior Data and Analytics Engineer translates complex business and data requirements into scalable, secure, high-performing analytics and data engineering solutions for AbbVie’s Operations (Supply Chain) organization. The role partners with DATA teams, Business Technology Solutions (BTS), business stakeholders, architects, and delivery teams to understand strategic priorities and deliver modern data solutions supporting analytics, business intelligence, and AI use cases. The engineer applies hands-on technical expertise and strategic thinking, using the North Star platform architecture to develop practical solutions aligned with enterprise architecture, development standards, data governance, security, and engineering best practices. The role provides technical leadership throughout the solution lifecycle, manages technical risks and dependencies, and communicates with technical and business stakeholders. Solutions span production-grade batch and near-real-time data pipelines; lakehouse and modern data warehouse architectures; governed data products; ingestion and orchestration; data quality, security, and governance; and DevSecOps practices. The position also owns solutions through production readiness, deployment, operational support, incident resolution, root-cause analysis, and continuous improvement.
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field plus 7 years of relevant experience; a master’s degree plus 6 years; or a PhD plus 2 years.
- 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.
- 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 supporting 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 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.
Nice To Haves
- 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.
- At least 5 years of hands-on experience designing and delivering enterprise modern data warehouse, lakehouse, data engineering, and analytical solutions.
Responsibilities
- Design, develop, test, deploy, and support production-grade data pipelines and data products for structured, semi-structured, and unstructured data, supporting batch and near-real-time or streaming patterns.
- Build reusable, parameterized pipeline components and automated recovery capabilities.
- Implement lakehouse and modern data warehouse solutions using Amazon S3, Apache Iceberg or other open table formats, AWS Glue Data Catalog, Amazon EMR/Spark, Amazon Redshift, and Redshift Spectrum.
- Apply medallion architecture, dimensional and domain-oriented modeling, workload isolation, partitioning, clustering and sort strategies, and performance optimization.
- Translate business domains into discoverable, interoperable, governed data products, defining product contracts, ownership, metadata, quality expectations, service-level objectives, lineage, and approved consumption patterns.
- Engineer reliable ingestion from enterprise applications, databases, files, APIs, and event streams.
- Develop orchestration using Apache Airflow, dbt, AWS Step Functions, or approved equivalents, including dependency management, idempotency, observability, alerting, and backfill capability.
- 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 company standards and regulated-industry expectations.
- 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.
- Partner with DATA, BTS, and 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.
- 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 and escalate material risks early.
Benefits
- Paid time off, including vacation, holidays, and sick time.
- Medical, dental, and vision insurance for eligible employees.
- 401(k) for eligible employees.
- Eligibility to participate in long-term incentive programs.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
