Lead Data Engineer

We are a specialized technology staffing agency supporting professional and financial services companies. Why do we stand out in technology staffing? We listen and act as advisors for our candidates on how they can best add value, find interesting projects, and pave a path for career advancement. We advocate for the best pay, diversity in tech, and the best job fit for every candidate we place. Our client, a leading investment firm, is seeking a Lead Data Engineer to join their team in El Segundo, CA! The ideal candidate will own the firm's Snowflake environment end-to-end: from platform architecture and performance to governance, strategy, and team uplift. Operating within an investment management environment, you will translate complex data requirements from portfolio managers, risk teams, and business leaders into scalable, well-governed solutions. Responsibilities Snowflake Platform Leadership Design, own, and continuously evolve the firm's Snowflake architecture — including data vault modeling, virtual warehouse strategy, cost governance, and performance optimization. Establish and enforce platform standards, coding conventions, access controls, and data quality frameworks. Drive adoption of advanced Snowflake capabilities — Dynamic Tables, Snowpark, data sharing, and native applications — where they create genuine business value. Act as the final technical authority on all Snowflake-related decisions across engineering and business teams. Data Architecture & Strategy Lead the design of the firm's broader data architecture: source-to-consumption layer design, medallion/layered patterns, and integration with upstream and downstream systems. Develop and maintain a forward-looking data strategy aligned with the firm's investment and operational priorities. Partner with senior leadership to define data roadmaps, prioritize initiatives, and communicate trade-offs clearly. Ensure architecture decisions are scalable, cost-effective, and compliant with data governance and regulatory requirements relevant to investment management. Remain hands-on; writing production-quality SQL, Python, and dbt code, building pipelines, and solving complex data problems alongside the team. Design and implement robust ELT/ETL pipelines ingesting market data, portfolio data, reference data, and operational feeds. Contribute to CI/CD practices, version control standards, and infrastructure-as-code approaches for data systems. Team Leadership & Business Engagement Provide technical mentorship and guidance to junior and mid-level data engineers, elevating the team's overall capability. Engage directly with business stakeholders — portfolio managers, risk, compliance, and operations — to understand needs and translate them into architectural decisions. Communicate complex technical concepts clearly to non-technical audiences; influence without authority across the organization. Contribute to hiring, onboarding, and the evolution of data engineering practices and culture. Qualifications Deep hands-on mastery of Snowflake architecture, performance tuning, security, and governance. Proven experience designing and implementing scalable data warehouse and lakehouse patterns in production environments. You can zoom out, identify what the data function needs to become, and build a credible path to get there. Comfortable in a room with senior stakeholders; able to challenge requirements constructively and drive clarity. No task is beneath you; you write code, review PRs, and debug pipelines alongside your team. Preferred Qualifications Investment management, financial services, or professional services background. Exposure to market data providers (Bloomberg, FactSet, Refinitiv) and financial data models. Formal team leadership or principal engineer experience. Experience in regulated environments with data compliance requirements. Experience with Agentic AI, RAG, or LLMs. Skills Advanced SQL, dbt, and modern ELT pipeline design. Python for data engineering and orchestration (Airflow, Prefect, or similar). Cloud data platforms (AWS, Azure, or GCP) and associated services. Data modelling: dimensional, data vault, and OBT approaches. Data governance, lineage, and cataloguing practices. #J-18808-Ljbffr Eleven Recruiting