Senior Data Engineer

Location

West Hollywood / Los Angeles, CA.

Department

Enterprise Technology – Data Engineering & AI

Employment Type

Full-Time

Work Model

On-Site (5 days per week)

Applicants must be legally authorized to work in the United States. Visa sponsorship is not available for this role.

About the Organization

Our client is a sophisticated private family office and investment management organization overseeing a diverse portfolio of investments, philanthropic initiatives, foundations, and operating activities.

The organization supports a broad ecosystem spanning private investments, finance, philanthropy, scientific initiatives, and operational programs, with hundreds of employees across its affiliated entities and significant annual expenditures.

We are seeking a highly motivated, innovative, and collaborative Senior Data Engineer to join the Enterprise Technology Data Engineering & AI team and play a pivotal role in building the organization's next generation of data and AI capabilities.

Summary

As a Senior Data Engineer, you will help shape a next-generation data and AI platform. You will architect and build cloud-native pipelines—including batch, streaming, and GenAI-enabled workflows—that transform structured and unstructured data from finance, HR, philanthropy, ERP/CRM systems, documents, and logs into trusted, self-service insights.

Leveraging modern data stacks and AI tools, you will drive advanced analytics, forecasting, and real-time dashboards while mentoring engineers and partnering closely with senior stakeholders.

Responsibilities

• Unified Data & AI Platform: Architect and develop a lakehouse stack and data management platform using tools such as Redshift or BigQuery, Airbyte, Airflow, or similar technologies.
• Streaming & Distributed Processing: Design real-time/streaming and batch pipelines supporting event-driven analytics and near-instant insights using technologies such as Kafka, Spark, or similar.
• End-to-End Pipelines: Build resilient ELT/ETL flows for relational data, semi-structured events, PDFs, images, and log streams, incorporating automated testing, lineage, and governance.
• GenAI by Default: Embed AI into routine workflows via MCP servers, including document intelligence, prompt-driven data-quality checks, automated documentation, AI-assisted code development, and conversational data exploration.
• Reporting & Dashboards: Deliver self-service semantic layers and pixel-perfect dashboards in Metabase, empowering stakeholders to explore data in real time.
• Advanced Analytics & Forecasting: Partner with Finance, HR, and Philanthropy teams to build predictive models, projections, and scenario analyses that support budgeting, portfolio strategy, and program outcomes.
• Stakeholder Partnership: Gather requirements, translate business logic into elegant data models, and promote data-engineering best practices across onshore and offshore teams.
• Technical Mentorship & Governance: Establish coding standards, review technical designs, and champion security, privacy, governance, and cost-aware architecture.

Requirements

• Bachelor's or Master's degree in Computer Science, or equivalent professional experience.
• 7+ years of experience designing and operating data platforms spanning structured and unstructured workloads, including documents and logs.
• Production expertise in data storage and processing pipelines using technologies such as:
• Prefect
• Airbyte
• Python
• Advanced SQL
• Redshift or BigQuery
• Kafka, Kinesis, or Pub/Sub
• OpenMetadata or comparable data monitoring/governance tools
• Mastery of dimensional and wide-table data modeling and performance tuning.
• Demonstrated experience using GenAI/LLMs for document processing, conversational analytics, code generation, or similar applications.
• Hands-on experience developing predictive models, time-series forecasts, scenario projections, and financial data analyses.
• Working knowledge of CI/CD using GitHub, containerization with Kubernetes/Helm, and infrastructure-as-code.
• Strong written and verbal communication skills, with the ability to connect technical architecture to executive strategy and collaborate effectively across technical and business teams.

Ideal Profile

The ideal candidate combines deep data engineering architecture expertise with hands-on execution. They are comfortable building modern cloud data platforms, working with both structured and unstructured information, implementing real-time and batch pipelines, and incorporating GenAI into production workflows.

This person should also be highly business-facing, capable of partnering with senior stakeholders across finance and other operational functions while providing technical leadership and mentorship to engineering teams.