Position: Data Platform Intelligence Manager, B2B
Reports To: Director of Data Platform Intelligence

Summary

We're seeking a people-first Engineering Manager to lead our high-performing Data Engineering team in a fast-growing proptech organization. This isn't a role for someone looking to stay hands-on in code, this is about leverage, not output. Your success equation is simple: Team Output × Team Health.

In this role, you will act as the core engineering partner to our B2B Product and Commercial organizations. You will move beyond simply fulfilling data requests to actively co-owning the roadmap for how data powers our enterprise offerings. Your team will build highly scalable data products that deliver actionable customer intelligence, optimize commercial performance, and provide deep behavioral insights.

Furthermore, you will champion the architectural and operational evolution of our foundational data platform. You will guide the transition from traditional, descriptive data pipelines into a proactive, AI-enabled intelligence engine. By embedding advanced analytics and machine learning capabilities directly into our core infrastructure, you will ensure our data ecosystem acts as a primary catalyst for product monetization, enhanced customer ROI, and long-term retention.

Our Tech Stack

Languages: Python, SQL
Orchestration: Dagster (migrating from legacy Airflow)
Compute & Storage: AWS Redshift, S3, Clickhouse, Athena
Transformation: dbt (Core/Cloud), Fivetran
Infrastructure: AWS (ECS/EKS, Glue, Lambda), Terraform with Terragrunt
AI/GenAI: AWS Bedrock, AWS Agentcore, LangChain

Key Responsibilities

1. Business Alignment & Strategic Delivery

Drive Business OKRs

• Move beyond "closing tickets" to delivering tangible business value. Ensure your team understands why they're building a pipeline, not just how.
• Partner closely with Product Managers to translate business requirements into technical roadmaps, prioritizing initiatives that directly impact company objectives.
• Own the "Universal Translator" role: Articulate technical debt, risks, and architectural trade-offs (e.g., cost vs. latency) in language that Product, Finance, and Executive teams understand.

Delivery Excellence

• Own the agile delivery process (Sprint Planning, Standups, Retrospectives), ensuring predictable, high-quality delivery of data initiatives.
• Collaborate with stakeholders to prioritize backlogs, balancing new features, tech debt, and infrastructure investments.
• Act as the primary point of contact for downstream data consumers (Data Science, Analytics, Product teams).

2. Systems Thinking & Operational Excellence

Build Resilient Systems

• Move the team away from "hero engineering." When something breaks twice, fix the system that allowed it to break, not just the symptom.
• Enforce coding standards, CI/CD practices, and architectural guidelines that scale.
• Proactively identify and eliminate bad processes, useless meetings, and low-value work dragging down velocity.

Own Platform Health

• Monitor and maintain SLAs, data freshness, pipeline reliability, and incident response processes.
• Lead Root Cause Analysis (RCA) with focus on systemic fixes and preventing recurrence.
• Champion Cloud Cost Optimization (FinOps) practices on AWS.

3. People Leadership & Talent Development

Coaching & Growth

• Manage, mentor, and coach a team of 6-8 Data Engineers, fostering a culture of technical excellence and psychological safety.
• Conduct meaningful 1:1s, performance reviews, and career planning, develop engineers into Senior and Principal roles.
• Your goal: Make yourself redundant in day-to-day operations by growing your direct reports into leaders.

Performance Management

• Set high standards. Believe that "speed with correction beats slowness with perfection."
• Address performance issues directly and constructively. Have difficult conversations when needed to maintain team excellence.
• Celebrate wins and create opportunities for engineers to showcase their work.

Recruiting & Retention

• Lead hiring efforts as a bar-raiser for talent, ensuring we hire engineers who fit our culture of ownership and autonomy.
• Drive onboarding processes that set new hires up for rapid impact.
• Build diverse, inclusive teams and create an environment where everyone can do their best work.

4. AI & Generative Intelligence Enablement

• Automate core workflows (documentation, data validation, metadata) to multiply team output.
• Build natural-language interfaces for frictionless, enterprise-wide data access.
• Architect scalable feature pipelines to power predictive models and monetization algorithms.
• Slash data cycle times using AI-assisted development and automation.
• Deploy LLM-powered observability and automated RCA to drastically reduce MTTR.

Essential Experience

Management Experience

• 5+ years of hands-on experience leading Data Engineering teams, with a proven track record of delivering complex data products end-to-end. Candidates new to people management or transitioning into their first leadership role will not be considered.
• Demonstrated success in hiring, developing, and retaining high-performing engineers.
• Experience managing remote or distributed teams preferred.

Technical Background

• 8+ years total experience in Data Engineering or Software Engineering.
• Deep understanding of the modern data stack: Python, SQL, Cloud Data Warehouses (Redshift/Snowflake/BigQuery), and Orchestration tools (Airflow/Dagster/Prefect).
• You don't need to be the best coder in the room anymore, but you must have the technical depth to review code, challenge estimates, and spot architectural risks.

Methodology & Operations

• Strong experience with Agile/Scrum methodologies and project management tools (Jira, Linear, etc.).
• Experience managing SLAs and production incidents in high-stakes environments.
• Track record of establishing operational excellence through monitoring, alerting, and incident response.

Desired Capabilities (Nice-to-Have)

• Background in proptech, real estate, or two-sided marketplace platforms.
• Experience with streaming data systems and event-driven architectures.
• Familiarity with ML infrastructure, feature stores, and GenAI integration in data workflows.
• Knowledge of data observability and quality frameworks (Monte Carlo, Great Expectations).
• Exposure to FinOps practices and cloud cost optimization strategies.