Job Description

The Staff Software Engineer – Data Platform is an individual contributor responsible for leading the design, modernization, and implementation of enterprise data platform capabilities. This role provides technical leadership in shaping scalable, secure, and reusable cloud-native platform solutions that align with business priorities, enterprise architecture, and engineering standards.
The role operates across software engineering, platform design, and implementation execution to advance modern data platform patterns, improve engineering consistency, and support delivery of enterprise analytics and data product capabilities. As a technical leader, this role influences design decisions across teams, drives adoption of modern engineering practices, and strengthens platform quality through automation, governance, and operational discipline.

Essential Responsibilities

· Lead design and implementation of enterprise data platform capabilities aligned to business priorities, platform strategy, and target-state architecture.

· Define and evolve reusable platform patterns, integration approaches, and software services that support enterprise-scale analytics and data products.

· Establish engineering approaches that improve scalability, security, reuse, service orientation, and delivery consistency.

· Translate business and technical requirements into architecture direction, implementation plans, and engineering outcomes.

· Guide solution design decisions by evaluating trade-offs across risk, cost, performance, supportability, and long-term maintainability.

· Support engineering teams in the design, build, optimization, and operation of enterprise data platform capabilities.

· Drive adoption of Infrastructure as Code, CI/CD, deployment automation, and engineering practices that improve platform maturity.

· Apply secure-by-design, governance, and compliance requirements in platform architecture and implementation decisions.

· Contribute to architecture governance and collaborate with enterprise architecture, security, and other technical stakeholders on platform direction.

· Evaluate emerging technologies and proof-of-concept opportunities for enterprise applicability and platform improvement.

· Enable Databricks and lake house capabilities across key platform patterns including ingestion, transformation, orchestration, optimization, and governed access.

· Promote observability, platform insights, and operational metrics to improve reliability, adoption, and cost efficiency.

· Mentor engineers and strengthen engineering discipline, technical quality, and architectural consistency across teams.

Qualifications / Requirements

· Bachelor’s degree in a STEM discipline or equivalent relevant experience in software engineering, cloud platform engineering, software architecture, or enterprise data platform environments.

· Professional experience in software engineering, platform engineering, cloud architecture, enterprise application engineering, or data platform design.

· Proven ability to lead technical direction across multiple initiatives, products, or platform capabilities and influence outcomes beyond an individual team.

· Strong foundation in software engineering principles and practices, including solution design, standards alignment, risk-based decision-making, and delivery oversight.

· Experience applying reusable design patterns, architecture practices, and platform engineering approaches to build scalable and supportable systems.

· Demonstrated success collaborating across engineering, enterprise architecture, security, and business functions in complex environments.

· Strong commitment to modernization, operational excellence, governance, and secure implementation practices.

· Strong communication and influencing skills, with the ability to explain technical recommendations and trade-offs to technical and non-technical stakeholders.

Technical Expertise

· Enterprise cloud data platform engineering and scalable platform design

· Apache Spark and PySpark, including distributed processing and performance optimization.

· Delta Lake, including schema management, optimization, lifecycle practices, and governed data operations.

· Python, Scala, Java, SQL, or similar languages for production-grade engineering solutions.

· AWS and/or Azure infrastructure and platform services relevant to enterprise data ecosystems

· Infrastructure as Code, configuration management, CI/CD, and deployment automation

· Databricks, cloud CLI tooling, REST APIs, workspace automation, and multi-environment governance

· Access control, identity, storage models, network connectivity, monitoring, observability, and cost/performance optimization.

Business Acumen & Leadership

· Provides technical leadership across platform capabilities through expertise, sound judgment, and execution focus.

· Translates business priorities into implementation direction, engineering decisions, and platform improvements.

· Influences technical direction across teams and helps build alignment on platform patterns, standards, and delivery approaches.

· Evaluates decisions through the lens of business value, scalability, risk, cost, and operational effectiveness.

· Leads through collaboration, technical credibility, and consistent engineering discipline rather than formal people leadership.

· Coaches others individuals to strengthen technical capability, architectural thinking, and delivery quality.

· Advances platform maturity through improved tools, patterns, automation, and engineering practices.

Personal Attributes

· Strategic and forward-looking mindset with practical execution focus

· Effective in complex, ambiguous, and fast-evolving environments

· Collaborative and influential across teams and stakeholder groups

· Open-minded, adaptable, and improvement-oriented

· Clear communicator able to simplify complex technical concepts

· Results-oriented and committed to secure, scalable, and business-aligned outcomes

Additional Information