Lead Software Engineer; Data and MarTech, Hybrid, Boston, Seaport District

Lead Software Engineer (Data and Mar Tech, Hybrid, Boston, Seaport District) #26561 PROJECT STACK and TEAM:

The Lead Software Engineer, Data and Mar Tech, is a senior technical leader embedded within a Scrum team, responsible for shaping how applications collect, process, and are delivered across the organization. This role sits at the intersection of software engineering, data architecture, and product delivery, ensuring that data systems are not only scalable and performant, but also reliable, well-modeled, and aligned to business needs.

• 7+ years of software engineering experience with a strong emphasis on data engineering
• Proven experience designing and operating scalable data pipelines and distributed data systems
• 3+ years of experience designing data solutions on AWS
• Proficiency in one or more programming languages such as Python, Java, or Scala
• Experience with modern data technologies (e.g., Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Big Query)
• Strong understanding of data modeling concepts, including dimensional modeling and data warehousing
• Experience working within Agile/Scrum teams and contributing to sprint planning and execution
• Hands on experience with real-time streaming and event-driven architectures
• Demonstrated ability to solve complex technical problems with minimal direction

• Experience implementing data governance, lineage, and cataloging solutions
• Exposure to machine learning workflows or data science collaboration
• Prior experience leading technical initiatives or influencing multiple teams
• Bachelor of Science in Computer Science or similar discipline

• Design, build, and maintain scalable data pipelines and data services (batch and real-time)
• Lead the architecture and evolution of data models, ensuring they support analytics, reporting, and product use cases
• Break down complex data initiatives into actionable work aligned with Scrum delivery practices
• Partner with Product, Analytics, and Engineering stakeholders to gather and refine requirements
• Establish and enforce best practices for data engineering, including schema design, pipeline orchestration, and testing
• Implement and maintain data quality frameworks, including validation, monitoring, and alerting
• Define and uphold SLAs/SLOs for data availability, latency, and accuracy
• Contribute high-quality production code and conduct thorough code reviews across the team
• Identify, surface, and mitigate technical risks that could impact delivery or system reliability
• Drive observability across data systems using logging, metrics, and tracing tools
• Mentor engineers and elevate team capabilities in data engineering practices
• Influence technical direction across teams and contribute to broader platform strategy