Lead Software Engineer - Data & AI Platform

Salary: $61, per year

• We need formal training or certification in software engineering concepts, plus 5+ years of hands‑on experience.
• We require practical experience delivering system design, application development, testing, and operational stability.
• We look for proven professional experience in software engineering or data platform development.
• We need advanced proficiency in one or more programming languages, including Python, Java, and SQL.
• We require hands‑on experience with distributed data processing frameworks such as Apache Spark and Flink.
• We need a strong grasp of data modeling approaches, including star schema and snowflake, as well as query optimization.
• We require experience designing and running data pipelines on Databricks using orchestration tools such as Apache Airflow.
• We need proficiency with cloud data services such as AWS S3, Glue, Redshift, Athena, EMR, and Lake Formation, or similar platforms.
• We require experience building production‑grade data platforms on Kubernetes with open catalog integration, such as Apache Iceberg, Unity Catalog, or Open Metadata, to support discovery, lineage, and governance.
• We need an advanced understanding of agile practices, including CI/CD, application resiliency, and security.
• We require experience developing Agentic AI, LLMs, RAG architectures, MCP, vector databases, and embedding‑based retrieval systems.
• Preferred: familiarity with data platform and transformation framework development.
• Preferred: experience with data mesh or data product architectures.
• Preferred: proficiency with Infrastructure as Code using Terraform and containerized deployments with Docker and Kubernetes.
• Preferred: experience with data observability, quality, and metadata management tools.
• Preferred: experience with semantic layers, metrics stores, or BI platforms such as Tableau and dbt Metrics.

• We execute creative software design, development, and technical troubleshooting, approaching problems beyond standard or conventional methods.
• We design, build, and maintain scalable data pipelines and ETL/ELT workflows for both batch and real‑time processing using Spark, Airflow, Kafka, and Flink.
• We develop data platform capabilities such as data cataloging, data quality frameworks, and semantic or metrics layers with governance, lineage, and compliance built in.
• We implement data modeling strategies, including fact and dimensional models and wide tables, to support analytics, reporting, and downstream use.
• We partner with analytics teams, product managers, and business stakeholders to turn data needs into production‑ready solutions.
• We produce secure, high‑quality production code and review and debug code written by others.
• We identify opportunities to remove or automate remediation of recurring issues to strengthen operational stability across applications and systems.
• We lead evaluation sessions with vendors, startups, and internal teams to assess architectural designs, technical strengths, and fit within our systems and information architecture.
• We lead development of the Agentic Autonomous Lakehouse capability, automating governed self‑service pipeline provisioning and lakehouse operations such as health, cost, performance analysis and best‑practice enforcement.
• We lead communities of practice across Software Engineering to promote awareness and adoption of emerging and leading‑edge technologies.
• We contribute to a team culture rooted in diversity, opportunity, inclusion, and respect.

• Agentic AI
• AI
• Airflow
• AWS
• Redshift
• CI/CD
• Cloud
• Databricks
• Docker
• ETL
• Flink
• Support
• Java
• Kafka
• Kubernetes
• MCP
• Python
• RAG
• SQL
• Security
• Snowflake
• Spark
• Tableau
• Terraform
• Unity
• dbt
• Game Dev
• Embedded

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