Jobs at Blackbuck Insights LLC

Provides technical leadership for Python software development, including designing scalable server-side applications, defining technology approaches, reviewing code, mentoring engineers, resolving performance issues, and deploying solutions across test and production environments. Collaborates with architects, product owners, business stakeholders, and external teams while contributing to roadmaps, prototypes, documentation, automation, risk mitigation, and continuous development-process improvement.

Develop and maintain Java applications by defining requirements, designing scalable solutions, debugging, reviewing code, implementing security, supporting deployments, and participating in testing. The role requires Java 8+, Spring and Spring Boot, concurrency and asynchronous programming, SQL/PostgreSQL, CI/CD tools, Unix/Linux commands, and unit and integration testing practices.

Develop and execute manual and automated testing for complex enterprise applications. Responsibilities include test planning, framework and suite development, API and backend testing, exploratory testing, defect analysis, code reviews, automation strategy, and improving unit-test coverage. The role partners with development, DevOps, infrastructure, and business teams to assess impacts, ensure testability, and support quality across Agile and Waterfall software development environments.

Performs ETL and data warehouse testing, validating data pipelines, transformations, source-to-target mappings, reconciliations, data quality, batch jobs, and database loads. Creates test scenarios, executes functional and regression testing, manages defects, and collaborates with technical teams to resolve data issues. The role also supports test automation and requires strong SQL, ETL, dimensional modeling, and slowly changing dimension knowledge.

Designs, builds, and optimizes scalable data pipelines, ETL/ELT processes, integrations, and cloud data platforms. Ensures data quality, governance, observability, reliability, and efficient processing for analytics and AI/ML initiatives. Supports Meridian platform implementation, troubleshoots production issues, automates workflows, and collaborates with architects, analysts, product owners, and stakeholders. Provides technical mentorship, participates in architecture and code reviews, and promotes engineering best practices.

Leads technical delivery and Agile execution for a wealth management data ingestion platform. Coordinates developers, data analysts, QA data engineers, and DevOps teams; manages sprint planning, backlogs, dependencies, development lifecycle tracking, testing, deployments, risks, and stakeholder communication. The role translates technical requirements into actionable work, aligns enterprise interdependencies, and supports reliable delivery of ETL/ELT pipelines and reporting feeds using Azure DevOps and Microsoft Azure.

Designs, builds, governs, and supports scalable analytics and data engineering solutions on Microsoft Fabric and Azure. Responsibilities include developing ingestion pipelines, lakehouse and warehouse models, orchestration, real-time solutions, performance optimization, testing, monitoring, incident resolution, security, capacity management, and platform governance. The role partners with business and technical stakeholders, facilitates design workshops, documents architecture, mentors teams, and contributes to estimation and enterprise BI strategy.

Designs and leads enterprise data lake and Lakehouse architecture on Microsoft Fabric and OneLake. Defines integration patterns, data models, ingestion frameworks, governance standards, lifecycle strategies, and performance and cost optimizations. Partners with engineers, analysts, data scientists, security teams, and business stakeholders to enable trusted self-service analytics, real-time insights, data science, and AI capabilities. Provides technical leadership, architecture reviews, reusable design patterns, and support for scalable data platforms and FinOps practices.

Leads data discovery, profiling, quality assessment, and Source-to-Target Mapping for wealth management data platforms. Analyzes complex financial datasets using advanced SQL, documents business rules and transformations, translates requirements for ETL and data engineering teams, supports data governance and testing, and communicates findings to technical and business stakeholders.

Leads data discovery, profiling, quality assessment, and Source-to-Target Mapping for enterprise wealth management platforms. Analyzes client, portfolio, transaction, holdings, performance, and billing data using advanced SQL; documents business rules and transformations; partners with ETL and data pipeline engineers; supports data governance, validation, testing, and stakeholder communication.

Develop and maintain enterprise Java applications and scalable backend solutions. Build REST APIs with Spring Boot, write unit and integration tests, troubleshoot production defects, participate in code reviews and Agile ceremonies, and contribute to Maven builds and Jenkins CI/CD pipelines. Collaborate with engineers, architects, and product teams on requirements, design, implementation, performance tuning, and continuous improvement.

Develop Java applications in a Linux environment, focusing on object-oriented design, object modeling, databases, REST web services, and high-volume, high-concurrency systems. Use development tools including Git, Artifactory, Maven, Jenkins, Confluence, and SonarQube. Spring Framework experience is preferred, while Angular knowledge is a plus.

Develop and maintain ETL and data warehousing solutions using the Ab Initio suite, including GDE, Co-Operating System, EME, and PDL. Work with SQL and Oracle relational databases, write Unix shell scripts, follow Agile practices, and use job scheduling tools such as Control-M or AutoSys.

Develop high-volume real-time and batch transaction systems using Core Java, J2EE, REST services, Spring, relational databases, and Unix/Linux environments. Responsibilities include applying object-oriented programming, multithreading, concurrency, test-driven development with JUnit, shell scripting, and continuous integration and delivery using Maven and Jenkins.

Develop and design robust Ab Initio solutions using advanced concepts such as project architecture, metaprogramming, vectors, parallelism, performance tuning, debugging, SMP/MPP architectures, and platform integrations. Use Linux and shell scripting, work with databases, and support integrations with Kafka and Redis. Collaborate across teams, document solutions, interact with senior management, and contribute within an Agile development lifecycle.

Lead the design and development of scalable machine learning infrastructure, including training, evaluation, deployment, model serving, feature stores, data pipelines, CI/CD, monitoring, and governance. Optimize production inference systems, ensure reliability and performance, collaborate with research and engineering teams, review architecture and code, and mentor junior ML engineers.

Leads end-to-end machine learning research, including problem definition, experimentation, model development, evaluation, and production deployment. Develops advanced models and scalable training pipelines using modern frameworks, collaborates with ML Engineering, MLOps, Product, and Data Engineering, and defines datasets and labeling strategies. Mentors junior researchers, reviews technical work, establishes reproducibility standards, and contributes to AI strategy, roadmap planning, and team development.

Owns finance and accounting operations for US and India entities, including financial close, reporting, FP&A, treasury, cash management, tax and regulatory compliance, audits, internal controls, and process improvement. Manages systems including QuickBooks Online, Tally, Excel, and Power BI while providing financial analysis, forecasts, dashboards, and strategic insights to leadership. Coordinates with internal stakeholders, auditors, tax advisors, banking partners, and external consultants across global operations.

Build and manage data pipelines, ETL workflows, and DAGs using dbt, Apache Airflow, Python, SQL, and Snowflake. Develop custom Airflow operators and hooks, perform data modeling and source-to-target mapping, and implement data quality and cleansing processes. Use cloud platforms, CI/CD, version control, and Snowflake utilities including Snowpipe, Tasks, Streams, stored procedures, and data sharing. Collaborate across teams, communicate insights, solve complex problems, and participate in on-call escalation.