Jobs at Blend360

Lead the design and implementation of scalable Java backend and distributed systems handling large-scale data processing. Build big data pipelines, optimize concurrent applications, manage Kubernetes workloads, and implement observability solutions. Responsibilities include architecture, performance tuning, production troubleshooting, deployment support, API and service integration, and mentoring engineers. The role requires strong expertise in Java, concurrency, distributed systems, SQL, Spark, Airflow, and Kubernetes, with additional experience in cloud, streaming, CI/CD, and infrastructure tools preferred.

Manage and optimize programmatic media campaigns across DSP platforms, including DV360. Monitor performance, analyze media quality and supply path metrics, produce reports, conduct QA, support vendor communication, reconcile billing, and collaborate with creative, analytics, agency, and internal teams. The role also contributes to campaign documentation, governance, and evaluation of emerging privacy-focused advertising technologies.

Lead the design and development of scalable Java backend systems, distributed architectures, concurrent applications, and high-volume data pipelines. Manage Kubernetes workloads, optimize performance, implement observability, support production deployments, and collaborate on APIs and integrations. Mentor engineers and drive continuous improvement. Requires advanced Java, distributed systems, concurrency, SQL, Spark, Airflow, Kubernetes, and performance optimization expertise, with 5+ years of backend or related engineering experience.

Own the strategy, roadmap, requirements, and development of a Snowflake data clean room product. Partner with data science, data engineering, and external partners to define privacy-safe data sharing, matching, onboarding, interoperability, testing, and documentation. Translate technical and business needs into product requirements, improve scalability and usability, and support partner adoption across Snowflake, Amazon, and The Trade Desk ecosystems.

Own strategy, roadmap, development, and go-to-market execution for four Latis audience and media data products. Partner with Data Science, Data Engineering, Sales, Marketing, agencies, and brands to define requirements, prioritize capabilities, launch products, enable commercial teams, gather customer feedback, and evolve solutions for audience targeting, activation, and campaign measurement.

Own the identity product strategy and roadmap for Latis, including its Network Reference Graph proof of concept, Identity Graph, and integrations with external identity providers. Define requirements for identity resolution, matching, graph construction, interoperability, and data connectivity. Lead cross-functional execution with integration engineering, data science, and AI teams; manage technical roadmaps, POCs, risks, and partner integrations. The role may also guide tag management strategy and build-versus-buy evaluations.

Supports email and direct mail campaign execution from creative routing and approvals through QA, testing, and launch. Manages workflow controls, file transfers, vendor coordination, campaign timelines, and final quality audits. Assists with dynamic email automations, beta testing, content accuracy, and creative trafficking while collaborating with clients and internal stakeholders. Requires strong organization, project and workflow management, email marketing experience, and attention to detail.

Build, maintain, and optimize scalable data pipelines, reusable code components, and analytics data models using Databricks or Snowflake. Refactor legacy code, automate GitHub repositories, standardize KPIs and metrics, and ensure data quality, performance, and reliability. Collaborate with analytics and engineering teams to improve data architecture and processes while independently driving initiatives.

Own the product lifecycle from scope definition through delivery and product-market fit. Define roadmaps, epics, workflows, requirements, success metrics, and documentation while coordinating clients, users, engineering, and domain experts. Prioritize competing demands, manage risks, triage feedback, validate AI-enabled features, and translate technical trade-offs for business stakeholders. The role requires strong product management experience, stakeholder leadership, and familiarity with AI/LLM product development in regulated environments.

Own the product roadmap, scope, epics, features, documentation, prioritization, and delivery of AI-enabled software products. Partner with clients, users, engineering, SMEs, and executives to define requirements, evaluate AI capabilities, manage risks, track product-market fit, and ship incremental value. Translate technical trade-offs into business language while coordinating strategic roadmaps and tactical sprint execution in regulated, ambiguous environments.

Own the product scope, roadmap, requirements, prioritization, documentation, and delivery of AI-enabled software products. Partner with clients, users, engineering, SMEs, and executives to validate workflows, manage risks, evaluate AI capabilities, track product-market fit, and ship incremental releases. Translate technical constraints into business language while coordinating strategic planning, sprint execution, feedback triage, and stakeholder decisions in regulated environments.

Leads fraud analytics for financial services clients by analyzing large payment and card datasets, identifying fraud trends and emerging risks, developing fraud strategies, and optimizing decision systems. Partners with fraud, risk, operations, and technology teams to monitor models, evaluate performance, create dashboards, and deliver actionable recommendations that reduce losses and improve customer experience.

Design, develop, and deploy scalable AI-powered applications, backend systems, APIs, integrations, and AI workflows. Translate ambiguous business needs into technical solutions, prototype AI products, contribute to architecture and engineering standards, improve scalability and reliability, collaborate cross-functionally, evaluate emerging AI tools, and mentor junior engineers.

Analyze debit card transaction and customer data to measure product performance, develop dashboards and KPIs, identify growth opportunities, and recommend strategies to improve acquisition, activation, engagement, retention, and usage. The role partners with product and cross-functional stakeholders, supports roadmap enhancements, monitors industry trends, and presents actionable insights to business leaders.

Develop and optimize recommendation systems and machine learning models for business applications. Work with large datasets using Python and SQL, integrate LLM capabilities, and apply MLOps practices to move solutions from experimentation into production. Collaborate with data and software engineers, use cloud technologies including Docker and Kubernetes, monitor model performance, and communicate technical decisions and trade-offs.

Evaluates analytical and fraud models, compares performance, prioritizes models for implementation, and coordinates deployment into production. Manages timelines, requirements, dependencies, deliverables, and issue resolution across data science, technology, modeling, and business teams. Tracks model performance before and after implementation, analyzes results, prepares reports, and communicates recommendations to stakeholders.

Analyze campaign, customer, prospect, and audience data using Python and SQL; build and validate marketing audiences; manage campaign operations, data quality, reporting, dashboards, and automation. Partner with marketing stakeholders to optimize campaigns, investigate data issues, communicate insights, and establish scalable execution and quality-control frameworks.

Analyze campaign, customer, prospect, and audience data using Python and SQL; build and validate marketing audiences; manage campaign operations, quality control, reporting, dashboards, and automation. Develop insights for campaign optimization, maintain data mappings and metadata, troubleshoot data issues, and collaborate with marketing stakeholders to support strategy and execution.

Analyze large customer, prospect, audience, and campaign datasets using Python and SQL. Build and validate marketing audiences, manage targeting and suppression logic, ensure data quality, and support end-to-end campaign execution. Develop automated reports, dashboards, queries, and scripts to monitor campaign performance and KPIs. Collaborate with marketing and cross-functional stakeholders to resolve data issues, translate campaign objectives into analytical solutions, and present actionable insights.

Owns paid search tracking, tagging, conversion measurement, pixel governance, and QA across SA360, Campaign Manager 360, Google Ads, and Microsoft Advertising. Manages Floodlight, UET, conversion, and campaign tags through enterprise tag management systems; establishes measurement standards and documentation; troubleshoots attribution and reporting discrepancies; ensures privacy compliance; and partners with marketing, analytics, technology, governance, and compliance teams.