Python/Data Engineer - SMA Solutions, Associate

BlackRock
2 LocationsPosted 2 April 2026

Job Description

About this role About BlackRock SMA Solutions: At BlackRock SMA Solutions, our strategies are designed to put our clients’ and their clients’ interests at the center of our investment advice; to minimize costs and taxes; and to incorporate each client’s unique values-aligned preferences into their investment portfolio. Offering a full suite of tools for our clients, from direct-indexing to active equity and fixed income strategies—with evolution a constant part of our game. As a business, we are fast-growing and our systems need to grow along with it. We are looking for engineers who like to innovate and seek complex problems. We recognize that strength comes from teams with a variety of perspectives, and will embrace your unique skills, curiosity, drive, and passion while giving you the opportunity to grow technically and as an individual. Engineers looking to work in the areas of orchestration, data modeling, data pipelines, APIs, storage, distribution, distributed computation, consumption and infrastructure are ideal candidates. About this Role: We are expanding the team of engineers that owns and operates our extensive data platform underpinning the SMA business. As a member of this team, you will build, enhance, and support a wide variety of data-centric applications and tools to generate or consume data feeds from diverse systems; generate reporting artifacts at scale; and expand the data estate in support of our users. You will work with your team and your stakeholders to ensure data arrives in a complete and timely manner, with validated quality. Requirements: BA/BS in Computer Science or equivalent practical experience At least 3 years of post-university experience as a full-stack engineer Solid knowledge of programming fundamentals—algorithms, data structures, design patterns, and paradigms Strong knowledge of Python Solid knowledge SQL and relational databases Ability to troubleshoot problems in a live environment and provide real time help with critical issues Write code that is easily understood and maintainable by other team members Keep up to date with developments in technologies we are using Ability to communicate and work effectively, supporting various business departments Ability to work in fast-paced interdisciplinary environment Desirable additional qualifications: Knowledge of Extract-Transform-Load ( ETL ) and big data analytics tools Experience working in a cloud environment (AWS, Azure, GCP) Familiarity with popular Python data analytics frameworks Familiarity with Snowflake or other large analytics engines Experience with data warehousing and working in environments with large scale, complex analytics requirements. Familiarity with DAG-based job scheduling tools Cloud-native container orchestration platforms and tools Experience working in the Financial Services industry 
For San Francisco, CA and Sausalito, CA Only the salary range for this position is USD$132,500.00 - USD$162,000.00 . Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits. BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance.
 Our benefits To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about. Our hybrid work model BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups m ... (truncated, view full listing at source)
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