Senior Software Engineer – PIMS

Publix Super Markets, Inc., the largest employee-owned company in the U.S., is driven by a dynamic technology team of 2,300+ professionals delivering innovative solutions to 1,400+ stores and 200,000+ associates across 8 states.

From IT security and platform engineering to architecture, software development, and infrastructure, we offer career opportunities at every level—internships through technical leadership.

Join a company consistently ranked among Fortune’s “100 Best Companies to Work For” and help us build more than great subs—build the future of technology at Publix.

Join the PIMS2 Data Service as a Senior Software Engineer opportunity. In this role the Senior Software Engineer will be responsible for the setup of Azure APIM and Data Lake/Databricks. Within the team, daily responsibilities include: data ingestion from Kafka topics into data lake (Lakehouse architecture). This also includes design, builing optimized PySpark pipelines for ingestion and transformations. We are also implementing patters for bronze/silver and gold performance optimization.

Location:

Hybrid (Lakeland, FL)
Work Model:
Enjoy the best of both worlds—collaborate in person and innovate remotely.

Why Join Us?

• Hybrid Flexibility:
  Work remotely when you need focus time and join us onsite for high-impact collaboration and brainstorming sessions.
• Operational Efficiency:
  Ensure technology solutions support efficient workflows and enable automation to improve operational effectiveness.
• Empowered Culture:
  We value autonomy, creativity, and continuous learning—your ideas shape the future of technology in our organization.

Your Impact

• Design, build, and optimize PySpark pipelines in Azure Databricks for ingestion, transformation, and publishing.
• Implement Delta Lake Lakehouse patterns (Bronze/Silver/Gold) and drive performance/cost optimization.
• Implement Spark declarative pipelines (Databricks declarative pipeline patterns; formerly DLT) and enforce data quality gates (expectations/validation) to ensure trusted datasets.
• Configure and support governance practices in Databricks (Unity Catalog permissions/access control concepts, auditability).
• Deliver curated/semantic datasets for analytics and downstream consumers; ensure data contracts and consistency.

#LI-JB1

• Bachelor’s degree in computer science, business, or a related analytical field or equivalent experience
• 5+ years experience in analysis, design, and coding
• 7+ years building scalable, production-grade data platforms and pipelines
• 5+ years Apache Spark / PySpark (batch processing, performance tuning, optimization)
• 5+ years ETL/ELT development, data modeling, and transformation patterns/frameworks
• 3+ years Azure Databricks (jobs/workflows, clusters, environment management)
• 2+ years data governance/catalog concepts in Databricks (Unity Catalog permissions/RBAC, auditing/lineage concepts)
• Strong Delta Lake / Lakehouse experience (Bronze/Silver/Gold, MERGE, schema evolution, OPTIMIZE/Z-ORDER basics)
• Azure fundamentals for data engineering (ADLS Gen2, identity/service principals/managed identity, secrets/Key Vault)
• Hands-on experience building/operating Data Quality Engineering (DQE): validation rules, reconciliations, and automated quality gates in pipelines

• Experience of warehousing processes and systems
• Experience of infrastructure and systems concepts
• Experience performing data analysis through creating and executing queries, interpreting results, and communicating findings to technical and business audiences
• Experience working as a Scrum Master within an Agile Scrum framework and its application in product development and delivery
• Experience utilizing Agile project management and code management tools (e.g., Azure Dev Ops or Jira)
• Experience managing 3rd party vendor relationships is an advantage
• Experience with Spark declarative pipelines (Databricks declarative pipeline patterns; formerly DLT/Delta Live Tables) including expectations-style rules and incremental processing.
• Experience implementing DQE patterns: completeness/accuracy checks, anomaly…