Associate - Data Engineering, Mumbai
Mumbai, Maharashtra, IN
5d ago

About this role

BlackRock is a leader in investment management, risk management and advisory services for institutional and retail clients worldwide.

As of Q1 2019, BlackRock’s AUM was $6.52 trillion. BlackRock helps clients meet their goals and overcome challenges with a range of products that include separate accounts, mutual funds, iShares® (exchange-

traded funds), and other pooled investment vehicles. BlackRock also offers risk management, advisory and enterprise investment system services to a broad base of institutional investors through BlackRock Solutions®.

Headquartered in New York City, as of 15th May 2019, the firm had approximately 14,900 employees in 30 countries and a major presence in key global markets, including North and South America, Europe, Asia, Australia and the Middle East and Africa.

DataOps Engineering (DOE)

When BlackRock was started in 1988, its founders envisioned a company that combined the best of financial services with powered by technology.

BlackRock has become a FinTech platform in the years since for investment management and technology services for thousands of users around the world.

Data is at the core of the Aladdin platform and our ability to consume, store, analyze and gain insight from data is a key component of our competitive advantage.

The DataOps Engineering team is responsible for building and maintaining a cutting-edge data platform that provides quality data to all users of the Aladdin platform, including investors, operations staff, data scientists, Aladdin applications and engineers.

Our goal is to provide highly available, consistent data of the highest quality to our clients, while evolving our platform to deliver exponential scale to the firm and powering the future growth of Aladdin.

We engineer high performance data pipelines, provide a fabric to discover and consume data and continually evolve our data storage capabilities.

Data Production Engineering (DPE)

Data Production Engineering team within DataOps Engineering is focused on orchestrating data flow and delivery connecting our supply chain and operators with partner systems and tools spanning financial modeling, accounting, and reporting.

The Data Production Engineering is responsible for Aladdin data processing, automation and optimization techniques, including dependency and configuration management and scalable delivery.

The team builds and own instrumentation, predictive analytics, and reporting to enable production monitoring and the drive towards continuous process improvement.

Job Description and Responsibilities :

  • Design, build, and maintain Aladdin batch cloud and analytics platform, working closely with end user requirements
  • Implement new business functionality to meet business and customer requirements, working closely with end users, with corresponding clear and concise documentation
  • Analyze and improve performance of applications and related operational workflow
  • Diagnose, research and resolve software defects
  • Leverage business intelligence and big data analysis to analyze trends in data pipeline, infrastructure capacity, etc.
  • for further scaling, optimization and improvement.

  • Ensure software stability via documentation, code reviews, regression, unit and user acceptance testing so production operations run successfully.
  • Provide Level 2&3 production support for all aspects of the platform that the team are responsible for existing processes and new business opportunities
  • Be a self-starter and work with minimal supervision in a team environment
  • Role Essentials :

  • A passion for engineering highly available, performant systems with a Student of Markets and Technology attitude
  • Bachelor’s / Master’s degree in Computer Science or Engineering or a related discipline
  • 2+ years of expert hands-on experience with scripting and data analysis with Python / Perl and open source framework in software design and development.
  • Experience in financial services preferred (investment banking / asset management).

  • Knowledge in relational database development and at least one NoSQL Database (e.g. Apache Cassandra, MongoDB, etc.)
  • Experience with big data stack to enable data analytics and business intelligence tools and frameworks would be preferable
  • Knowledge of software development standard methodologies (analysis, design, development, testing) and basic understanding of Agile / Scrum methodology and practices
  • Enthusiasm and ability to work in a team-environment, multi-tasking, collaboration and effective communication skills are a must.
  • Excellent analytical and problem-solving skills
  • If interested, all candidates must apply through your career services office. In addition, you MUST apply online through our career site at and submit a copy of your most recent resume and cover letter.

    BlackRock is proud to be an Equal Opportunity and Affirmative Action Employer. All qualified applicants will receive consideration, including minorities, women, protected veterans and individuals with disabilities.

    About BlackRock

    BlackRock’s purpose is to help more and more people experience financial well-being. As a fiduciary to investors and a leading provider of financial technology, our clients turn to us for the solutions they need when planning for their most important goals.

    As of June 30, 2019, the firm managed approximately $6.84 trillion in assets on behalf of investors worldwide. For additional information on BlackRock, please visit Twitter : blackrock Blog : LinkedIn : .

    BlackRock is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.

    BlackRock will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the requirements of the law, including any applicable fair chance law.

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