Data Analytics Engineering Manager

Top Skills: Snowflake, SQL, ETL, Azure, Data Governance, Investment, Banking

About the Team:

The Data Delivery and Operations Division partners across Investment Management, Operations, Risk, and Technology to deliver trusted, timely, and analytics-ready data that powers investment decisions.

The Data Analytics Engineering team serves as enterprise owner of investment and reference data within the analytics environment. The team transforms raw data into governed, analytics-ready assets that support reporting, performance measurement, risk analysis, accounting, and downstream investment platforms.

Position Overview:

The Manager, Data Analytics Engineering provides overall leadership and accountability for investment data and analytics engineering function.

This role owns the integrity, availability, and quality of investment data assets, including reference data, security master, and entity master domains. The Manager is accountable for data quality SLAs, daily data validation, and the reliability of analytics-layer data supporting investment, trading, risk, and portfolio management activities.

The Manager plays a critical role in overseeing the start-of-day data process to ensure investment teams have accurate and complete data to support daily trading and portfolio management decisions. This includes leading a 24x6 operational support model for data processing, validation, issue triage, and resolution.

This position also provides strategic oversight of key vendor relationships supporting investment and reference data, including SimCorp. The Manager ensures vendor performance aligns with SLAs, data quality expectations, and enterprise investment requirements.

The successful candidate combines strong investment data expertise, operational leadership capability, and proven people management experience. This role routinely applies significant judgment to moderate-to-complex business and data challenges and plays a central role in enterprise data governance and operational readiness.

Essential Activities:

Investment Data Ownership & Master Data Management:

• Serve as enterprise owner of analytics-layer investment data, including reference data, security master, entity master, holdings, and related datasets.
• Lead single source of truth strategy for security master and related master data domains.
• Establish governance standards, stewardship models, and documentation practices for master data assets.
• Rationalize redundant or conflicting data sources to improve consistency across the organization.
• Ensure alignment between master data architecture and investment, performance, risk, and accounting requirements.

Business Translation & Analytics Engineering:

• Partner closely with Investment Management, Operations, Risk, and ETL Engineering to translate business requirements into scalable analytics-ready data models.
• Partner with ETL Engineering and Technology to ensure analytics transformation processes align with enterprise architecture and operational standards.
• Provide architectural oversight for the analytics transformation layer to ensure data models, validation processes, and documentation practices meet enterprise standards.

Data Quality & Governance:

• Design and implement enterprise data quality frameworks within the analytics layer.
• Own and report on data quality SLAs, including timeliness, completeness, and accuracy metrics.
• Develop and publish recurring SLA and data quality reporting for leadership and stakeholders.
• Ensure data validation controls are embedded in daily processing workflows.
• Collaborate with upstream data providers and ETL Engineering to remediate systemic data issues.
• Embed governance controls into analytics datasets to improve auditability and transparency.

Vendor & Platform Relationship Management:

• Maintain strategic oversight of major vendor partners providing investment and reference data, including SimCorp.
• Ensure vendor deliverables meet contractual SLAs and enterprise data quality expectations.
• Lead regular governance meetings and performance reviews with SimCorp and other key vendors.
• Partner with Technology and Investment Operations to address vendor-related data or platform issues.

Downstream Platform Support & Operational Readiness:

• Own the start-of-day data readiness process in support of daily trading and portfolio management activities.
• Lead a 24x6 operational support model for data processing, validation, issue escalation, and resolution.
• Establish clear escalation procedures and incident response protocols for data-related production issues.
• Ensure analytics-layer datasets support reliable exports to downstream systems, including Order Management Systems (OMS), risk analytics platforms, and other investment technologies.
• Partner with Technology and ETL Engineering to maintain stable, governed export processes.

Cloud & Modernization Strategy:

• Co-design future-state cloud data architecture in partnership with ETL Engineering and Technology.
• Define clear architectural swim lanes between ingestion/orchestration (ETL Engineering) and transformation/governance (Analytics Engineering).
• Collaborate with ETL Engineering and Technology to support controlled deployment processes and production governance standards.
• Advance automation, reliability, and engineering discipline across analytics workflows.

Staff Leadership & Development:

• Lead, mentor, and develop a team of analytics engineering professionals.
• Conduct annual performance reviews and provide ongoing coaching.
• Elevate technical standards across the team in investment data modeling and governance practices.
• Align hiring profiles with evolving operational and enterprise data needs.
• Foster a culture of accountability, documentation, peer review, and continuous improvement.

Qualifications Include:

• Bachelor s degree in Data Analytics, Engineering, Information Systems, or related field; advanced degree or certifications preferred.
• 8 12+ years of progressive experience in analytics engineering, data architecture, or investment data management.
• Advanced SQL expertise and strong dimensional modeling experience.
• Experience working with cloud-based data platforms (Azure, Snowflake, or comparable platforms).
• Experience with master data management concepts and enterprise data governance.
• Strong understanding of investment industry data, including security master, holdings, benchmarks, performance, and risk.
• Demonstrated experience managing and developing technical staff.
• Familiarity with modern production governance and controlled deployment practices preferred.

Salary/Rate: $150,000-$160,000 (depends on experience level) plus benefits.

About The Company

Peterson Technology Partners (PTP) is an Equal Opportunity Employer committed to creating a transparent, inclusive, and human-centered hiring experience.

For more than 28 years, PTP has operated as one of the top IT staffing and recruiting firms in the USA built on trust, long-term partnerships, and technical excellence.

Based in the Chicago suburb of Park Ridge, IL, our team of more than 500 employees and consultants is dedicated to:

Helping every client make the best hiring decisions possible

Matching professionals with the right IT jobs and career opportunities

As part of that commitment, we believe in providing clear information about how our hiring technologies work and how your data is used. The following section outlines our AI-assisted interview process and your rights as a candidate.

AI-Assisted Interview Experience (Pete & Gabi Rebecca)

To provide a consistent, fair, and flexible experience for all candidates, we use AI-assisted tools to support parts of the interview process. This includes our proprietary AI platform Pete & Gabi, which includes AI recruiter Rebecca.

These AI hiring tools help us:

• Transcribe interviews
• Summarize candidate responses
• Generate job-related insights
• Streamline communication and scheduling

Please note that:

The AI does NOT make hiring decisions; all decisions are made by our human recruiters, hiring managers, or client partners.

The AI does not evaluate facial expressions, emotions, or physical traits; it is used only to support fairness, consistency, and efficiency.

If you prefer a non-AI interview format, we will gladly provide an alternative.

Technical or Case Interviews (Role-Dependent):

When applying for certain tech jobs, you may participate in:

• A technical interview
• A coding challenge
• A case study
• A client-specific assessment

We will always explain what to expect in advance so you can prepare with confidence.

Human Review & Selection:

Every candidate's profile including interviews, conversations, and assessments is reviewed by experienced recruiters and hiring leaders.

AI insights may assist with organization and evaluation, but final decisions are always human-driven.

Your Rights as a Candidate:

At PTP, every candidate has the right to:

Request a non-AI interview path

Ask how your data is being used

Request access to transcripts or interview recordings

Request deletion of your AI-recorded interview

Receive clear, timely communication

Our goal is to ensure you feel respected, informed, and supported throughout your experience.

Our Commitment:

For more than 28 years, PTP has focused on putting people first candidates, consultants, employees, and clients.

We're committed to a hiring process that is:

• Transparent
• Compliant
• Equitable
• Powered by innovative technology that enhances not replaces human judgment

Welcome to the future of hiring at Peterson Technology Partners.

We're excited to learn more about you.

Equal Employment Opportunity:

Peterson Technology Partners is an Equal Opportunity Employer. All qualified applicants will receive consideration without regard to race, color, religion, national origin, gender identity, sexual orientation, disability, veteran status, or any other protected characteristic.