Manager, Analytics Software Engineering

Serve as a hands-on technical leader who earns credibility through sound judgment, ownership, and execution.

Partner with engineers, data scientists, architects, and product leaders to guide architecture, resolve critical issues, and turn ambiguity into action.

Champion responsible AI-first thinking across products, engineering productivity, and business problem-solving.

Help teams apply machine learning, Generative AI, agents, recommendation systems, and predictive models to deliver measurable value.

Promote responsible, secure, scalable AI adoption while encouraging rapid experimentation and continuous learning.

Lead and develop software engineering, data science, and technical leadership talent across multiple geographic locations.

Establish clear decision rights, technical ownership, delivery accountability, and cross-geo collaboration.

Build an inclusive, high-accountability culture and support hiring, career development, performance management, and succession planning.

Own execution and operational health across strategic products, shared platforms, and technical workstreams, balancing near-term delivery with architecture investments.

Translate priorities into outcome-based roadmaps with clear milestones, ownership, dependencies, risks, and success measures.

Ensure visible execution while balancing speed, security, quality, scalability, reliability, and production readiness.

Partner with product, architecture, data, and business leaders to shape strategy and connect engineering investments to measurable outcomes.

Promote reusable platforms, scalable patterns, pragmatic technology choices, and shared capabilities.

Build strong partnerships with product, program, architecture, analytics, and business leaders.

Communicate roadmaps, trade-offs, progress, operational health, risks, and investment needs using measurable outcomes.

Align distributed teams around shared priorities, architecture, and execution discipline.

Experience leading enterprise applications, data platforms, and analytics solutions using modern software, data, and cloud technologies; familiarity with Python, C#, .NET, SQL Server, Azure Data Factory, Azure Databricks, Spark, or Power BI is beneficial.

Experience with modern data architectures, APIs, microservices, cloud-native applications, and large-scale structured and unstructured data.

Experience building or scaling machine learning, recommendation, predictive analytics, or intelligent decision-support solutions.

Familiarity with Azure OpenAI, LLMs, RAG, AI agents, vector databases, MLOps, model monitoring, and responsible AI.

Knowledge of DevOps, CI/CD, automated testing, observability, and production operations.

Ability to evaluate emerging technologies and apply them pragmatically to business opportunities.

Remains technically engaged in architecture reviews, prototypes, design discussions, production troubleshooting, and AI strategy without becoming the teams’ primary implementer.

Compensation: The target salary range for this position is $170,000 - $220,000. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU’s), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process.