Senior Engineering Manager, Data Fabric

Advisor360•Needham, MA

6h•$206,000 - $228,000•Onsite

About The Position

Advisor360° is looking for a Senior Engineering Manager to lead our Data Fabric team. This will be a senior people leader who sits at the intersection of technical depth, data governance, and organizational influence. This is not a role for someone who wants to stay in their lane. You'll own the development and reliability of the data infrastructure that powers our entire platform, lead a team of up to 12 engineers, and serve as a strategic voice for data across the company. You'll drive both execution and culture: running data development with the rigor of a software organization while building the kind of team that takes extreme ownership, moves fast, and ships with confidence. If you thrive at the intersection of high-stakes infrastructure, cross-functional partnership, and servant leadership — this is the role.

Requirements

8+ years of progressive experience in data engineering or data platform development, with at least 5+ years leading and growing data engineering teams.
Demonstrated track record in building and leading teams that have produced, published, and maintained data products consumed by multiple stakeholders or business units.
Delivered and supported mission-critical, highly available services in production environments — you know what it means to own uptime.
Experience driving cross-organizational change management: rolling out new platforms, frameworks, or ways of working across a company.
Familiarity with medallion architecture, semantic layers, and data modeling.
Sufficient technical fluency to ask the right questions, challenge assumptions, evaluate trade-offs, and keep the team moving.
Snowflake (required)
Azure (e.g. Azure Function, AKS, etc.), with familiarity across GCP and/or AWS
Azure Data Factory, dbt, Python, PySpark, REST / GraphQL microservices; real-time event-driven architectures using Kafka, Orkes, and Databricks
SQL Server, relational and analytical data stores (e.g. Postgresql); stream and batch pipeline design, Graph databases
Profiling, validation, remediation frameworks; cataloging, lineage, policy enforcement, and metadata management
Monitoring, alerting, and operational visibility across the data estate
SDLC methodologies (Agile, DevOps), code reviews, source control, build processes, testing, and CI/CD
Knowledge of using GenAI tools (e.g Claude or Augment) to speed up development.
Servant leadership: you lead by removing obstacles, elevating your team, and creating the conditions for others to do their best work.
Extreme ownership: you own outcomes end-to-end, take accountability without hesitation, and instill that same mindset in your team.
Systems thinking: you see the whole board: how infrastructure, governance, team dynamics, and business strategy connect and influence each other.
Exceptional communication: able to translate complex data concepts clearly for engineers, product teams, and the C-suite alike.
Business acumen: you understand the business context behind the data, and you use that understanding to prioritize what matters most.
Comfortable navigating ambiguity and evolving requirements; you know how to deliver a winning product even when the ground is shifting.

Nice To Haves

Familiarity with financial data sets and experience working with or producing high-quality finance/fintech data from multiple sources.
Knowledge of complex financial products in a wealth management or broker-dealer context.
Experience with AI-based automation for code quality improvement, predictive analytics, or data-driven decision-making.
Experience leading teams distributed across multiple time zones.
Technical expertise in designing and scaling distributed systems architecture.

Responsibilities

Lead, coach, and grow a team of up to 12 data engineers — setting a high bar for quality, ownership, and craft while actively enabling individual growth.
Run data development like a software organization: full SDLC adherence, CI/CD pipelines, structured dev/test/prod environments, sprint planning, Jira-based change management, and clear prioritization.
Hire great engineers who level up the team, and build the kind of culture where people do their best work.
Maintain team health, morale, and productivity as the ecosystem around you evolves quickly.
Lead the development, delivery, and ongoing maintenance of Advisor360°'s core data products infrastructure — the shared foundation that underpins all data deliverables across the organization.
Build and evolve the frameworks and methodologies that enable independent teams to develop their own data products while adhering to unified governance, quality, and architectural standards.
Own low-latency data pipeline development end to end, including tooling, monitoring, alerting, and maintaining a unified view across data sources.
Drive lifecycle management of enterprise data products serving internal teams, platform capabilities, and client-facing solutions.
Embed data quality, observability, ownership, and governance into every layer of the stack as a foundational design principle.
Establish and enforce trust frameworks that ensure data accuracy, lineage, and compliance across the enterprise.
Partner with compliance, risk, and security teams to ensure all data practices meet regulatory and industry standards.
Manage the support and maintenance of the data products that the team owns.
Partner closely with engineering, product, finance, go-to-market, and operations stakeholders to ensure data capabilities align with business objectives.
Serve as a company-wide advocate for data as a strategic asset — influencing technology decisions, product roadmaps, and organizational priorities.
Lead change management efforts that drive adoption of modern data practices and a shared data culture across all teams.
Work effectively with both technical and non-technical stakeholders, aligning on scope and setting clear, realistic expectations on delivery timelines.
Measure and maintain alignment between data management operations such as data products manufacturing, engagement, and the business value derived from the data products.
Ensure data management activities, specifically data product creation and use, are aligned with and demonstrably contribute to achieving the expected business value.