Senior Data and Analytics Engineer

Description

The company's Operations (Supply Chain) group needs a senior engineer to turn complex business and data needs into scalable, secure, high-performing analytics and data engineering solutions.

You will work with the internal DATA and BTS teams, business stakeholders, architects and delivery teams to set priorities and build modern data products that power analytics, BI and AI.

The role blends hands-on building with architectural judgment, applying the North Star platform design while keeping work aligned with enterprise architecture, engineering standards, governance and security.

You will lead technical direction across the solution lifecycle, manage technical risks and dependencies early, and explain trade-offs to both technical and business audiences.

Responsibilities

• Build and support production data pipelines and data products for structured, semi-structured and unstructured data, covering both batch and near-real-time or streaming needs, using reusable, parameterized frameworks with automatic recovery.
• Implement lakehouse and warehouse solutions on Amazon S3, Apache Iceberg or other open table formats, AWS Glue Data Catalog, Amazon EMR with Spark, Amazon Redshift and Redshift Spectrum, applying medallion layering, dimensional and domain models, workload isolation, partitioning, sort or cluster keys and tuning.
• Turn business domains into discoverable, governed data products with clear contracts, owners, metadata, quality targets, service levels, lineage and approved access patterns for dashboards, self-service, APIs, data science and AI.
• Build reliable ingestion from enterprise applications, databases, files, APIs and event streams, and orchestrate it with Apache Airflow, dbt, AWS Step Functions or other approved tools, handling dependencies, idempotency, monitoring, alerts and backfills.
• Add automated quality checks, reconciliation, schema validation, lineage, cataloging, retention rules, encryption, least-privilege access and audit trails, working with governance, security, privacy, architecture and platform teams to meet company and regulated-industry standards.
• Set up source control, code review, automated testing, CI/CD, infrastructure as code, environment promotion, release management and monitoring, and measure gains in reliability, cost, performance and developer speed.
• Partner with DATA, BTS and Operations stakeholders to turn requirements into scalable designs, give technical direction, resolve design and delivery problems, and help shape portfolio priorities and roadmaps.
• Own solutions end to end, from production readiness and deployment through support handover, incident response, root-cause analysis and reducing technical debt. Work independently and escalate material risks early.

Requirements

• A bachelor's degree in computer science, information systems, data engineering or a related field plus 7 years of relevant experience, or a master's degree plus 6 years, or a PhD plus 2 years.
• Several years of hands-on experience designing and delivering enterprise data warehouse, lakehouse, data engineering and analytics solutions.
• Advanced SQL and Python skills, with solid hands-on work in PySpark or Scala and distributed processing on Apache Spark, EMR or a comparable platform.
• Proven record building large lakehouse and warehouse systems with Amazon S3, Apache Iceberg or comparable open table formats, AWS Glue, Amazon EMR, Amazon Redshift and Redshift Spectrum.
• Proven work with medallion architecture, data modeling, schema design, incremental loads, change data capture, partitioning, performance tuning and cost optimization.
• Experience running production batch and streaming pipelines, including orchestration, retries, backfills, monitoring, alerting and operational support.
• Hands-on use of transformation and orchestration tools such as Apache Airflow, AWS Step Functions or approved equivalents.
• Experience with CI/CD, automated testing, Git-based development, infrastructure as code and Agile DevOps delivery.
• Demonstrated ability to build governed, AI-ready data products that serve BI, self-service analytics, data science, machine learning and contextual AI.
• 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.
• Preferred: 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 similar.
• Preferred: Working knowledge of 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 and context layers for AI applications.
• Preferred: Cloud or data engineering certifications, which are beneficial but not required.
• Preferred: 5+ years of hands-on experience designing and delivering enterprise data warehouse, lakehouse, data engineering and analytics solutions.

Conditions

Base pay is posted as a range where pay disclosure laws apply. Actual pay depends on factors such as location and may fall above or below the range, and the range may change.

Bonuses, incentives and other compensation are at the company's discretion and are not earned until paid.

The role is open to applicants in the US and Puerto Rico only.

The company is an equal opportunity employer that welcomes veterans and people with disabilities.

Benefits

• Paid time off covering vacation, holidays and sick leave, plus medical, dental and vision insurance and a 401(k) for eligible employees.
• Eligibility for long-term incentive programs.