Senior Lead Software Engineer, Data Engineering

What you'll do

• Design and deliver reusable frameworks for high-throughput real-time and large-scale batch data processing.
• Write production code daily as the senior technical contributor while ensuring high design quality and operational readiness.
• Lead a Scrum team by decomposing complex problems into deliverables and unblocking engineers without a formal reporting line.
• Build team technical capability by mentoring engineers and raising code-review standards.
• Provide recommendations on data management, governance, quality, profiling, and lineage procedures.
• Develop streaming and low-latency serving components using Kafka, Cassandra, and AWS services.
• Design and deliver trusted data collection and analytics solutions across AWS, Databricks, and Snowflake.
• Use enterprise-authorized generative AI to accelerate design, implementation, testing, and technical documentation.

What we're looking for

• Formal training or certification on software engineering concepts and 5+ years of applied experience.
• Strong hands-on programming expertise in Java, Python, and Scala.
• Demonstrated experience building reusable frameworks and platforms that other teams adopt and build on.
• Proven experience delivering high-throughput real-time systems and high-volume Spark processing over very large datasets.
• Deep hands-on experience with AWS (compute, serverless, managed data services, IAM) and Databricks.
• Hands-on experience with Snowflake in a production data platform environment.
• Working experience with Kafka, Cassandra, and related AWS streaming and NoSQL offerings.
• Experience working on AI/ML systems with a solid understanding of core model families and transformer models.
• Mastery of Agile/Scrum delivery in practice.
• Experience mentoring and growing engineers and building team technical capability without formal management authority (preferred).
• AWS, Databricks, and/or Snowflake certifications (preferred).
• Strong grasp of Data Governance, Data Quality, Data Profiling, and Data Lineage in regulated environments (preferred).
• Hands-on experience with feature provisioning for ML, including Feature Store on Unity Catalog (preferred).
• Experience adopting agentic AI development tooling on a team and measurably accelerating delivery (preferred).
• Exposure to cloud migration or platform modernization at scale (preferred).