Jobs at Anblicks

Drive end-to-end US W2 talent acquisition from intake through onboarding. Source and screen technical candidates, manage client interviews and feedback, negotiate offers, ensure hiring compliance, maintain ATS data and pipelines, and meet hiring targets and SLAs. The role requires strong knowledge of US work authorization, experience recruiting data and cloud professionals, excellent stakeholder management, and availability during US business hours.

Perform functional, regression, integration, end-to-end, and compatibility testing for Android and iOS applications. Develop and maintain Appium-based automation frameworks, validate APIs, troubleshoot network traffic with Charles Proxy, and analyze mobile logs. Design test cases, investigate defects, and support release quality. Apply AI tools to testing and evaluate LLM, RAG, and AI-agent features for accuracy, relevance, reliability, grounding, and hallucination risks.

Design and implement a C# and NUnit automation framework for simulator-based testing. Build reusable TCP/socket, XML messaging, database validation, logging, and fault-injection components. Automate resilience and chaos scenarios, integrate regression testing into GitHub Actions with gated execution, maintain traceability, and document manual-only cases while delivering under an aggressive timeline.

Own the backend API and data-serving layer for a customer-facing metrics platform. Build and evolve FastAPI services, PostgreSQL data models, migrations, authentication, tenant isolation, performance optimizations, and metric reconciliation. Contribute to React and TypeScript development, testing, observability, production support, and cross-functional analytics work. Ensure accurate, secure, performant reporting through end-to-end feature ownership and strong data validation.

Build and maintain full-stack applications using React/Next.js and Java Spring Boot or Node.js on AWS. Design serverless, event-driven architectures and DynamoDB data models; create testing pipelines and sandbox environments; implement secure, maintainable code; translate architecture decisions into production systems; document APIs, runbooks, and data models; and communicate effectively with clients and executive stakeholders.

Leads the architecture, development, and delivery of cloud-native full-stack applications using React or Next.js, Java Spring Boot or Node.js, and AWS services. Owns client engagements from requirements through production, makes architecture decisions, manages integrations, maintains technical documentation, builds testing and sandbox environments, mentors engineers, and presents solutions to senior stakeholders.

Leads end-to-end data engineering solutions, including scalable Snowflake and dbt pipelines, data modeling, SQL transformations, CDC, ELT, quality frameworks, monitoring, and production deployment. Designs engineering standards, troubleshoots complex data issues, improves performance and cost efficiency, collaborates with stakeholders, leads technical reviews, and mentors engineers.

Designs, develops, and optimizes scalable Snowflake and dbt data pipelines, ELT/ETL workflows, dimensional data models, and analytics warehouse solutions. Implements data quality, governance, security, CI/CD, monitoring, and documentation practices. Troubleshoots data issues, collaborates with analytics and business teams, and mentors junior engineers. Requires experience with Azure Data Factory and structured and semi-structured data formats.

Designs and deploys production AI solutions and data platforms, leads architecture and implementation discussions, presents technical solutions to customers and executives, evaluates AI trade-offs, and identifies revenue-growth opportunities within existing accounts.

Administer, monitor, and maintain the enterprise Snowflake environment, including access controls, resource monitors, warehouses, and account settings. Troubleshoot technical, performance, and access issues across teams; evaluate new Snowflake capabilities; and promote best practices for query performance, cost management, and governance. Document platform standards and operational procedures while collaborating with data engineering, analytics, and platform teams.

Own production support for SnapLogic and dbt data integration pipelines, including monitoring, incident response, ticket triage, root-cause analysis, SLA adherence, and performance optimization. Lead and mentor a small support team, manage on-call rotations, maintain runbooks, coordinate releases and change management, report operational metrics, and communicate issue status with engineering and client stakeholders.

Designs, develops, and optimizes scalable Snowflake and dbt data warehouse solutions, including ELT/ETL pipelines, dimensional models, data quality frameworks, CI/CD workflows, governance, and lineage documentation. Collaborates with analytics and business teams, troubleshoots data issues, establishes engineering standards, and mentors junior engineers. The role also requires Azure Data Factory experience and expertise handling structured and semi-structured data.

Leads U.S. go-to-market and demand-generation strategy for enterprise technology services. Owns ABM, integrated campaigns, partner programs, executive events, industry marketing, marketing operations, funnel reporting, and pipeline attribution. Collaborates with sales, SDRs, executives, and technology partners to generate enterprise opportunities and revenue. Builds the marketing operating model, team, processes, and supporting technology while translating complex Data, AI, Cloud, Analytics, and Digital Transformation services into compelling buyer messaging.

Designs, develops, and optimizes scalable data infrastructure, ETL/ELT pipelines, distributed processing workflows, and cloud data platforms. Owns identity resolution, data integration, modeling, governance, privacy, quality, lineage, and monitoring solutions. Collaborates with data science and analytics teams, leads architectural decisions, mentors engineers, conducts code reviews, and promotes reliable, secure, cost-efficient engineering practices.

Leads development of statistical and machine learning models for marketing measurement and optimization. Owns attribution, marketing mix modeling, causal inference, incrementality analysis, experimentation, predictive modeling, and optimization solutions. Works with large-scale media and customer datasets, identity and privacy-aware analytics, and production model monitoring. Collaborates with engineering and product teams, establishes validation standards, communicates insights to stakeholders, reviews technical work, and mentors junior data scientists.

Designs and maintains scalable ETL/ELT pipelines using SQL, Python, and cloud data platforms. Integrates data from multiple sources, implements quality and monitoring controls, troubleshoots performance issues, supports data modeling for analytics and machine learning, and follows security and governance practices. Collaborates with engineers, data scientists, and stakeholders while documenting pipelines and exploring AI-assisted development tools.

Develops, validates, and maintains statistical and machine learning models for business measurement and analytics. Responsibilities include exploratory data analysis, feature engineering, causal inference, attribution modeling, data quality validation, model monitoring, privacy-aware analytics, and communicating insights to stakeholders. The role collaborates with data engineering and cross-functional teams using large, multi-source datasets and cloud data platforms.

Designs and drives enterprise architecture strategies across applications, APIs, data platforms, integrations, and cloud environments. The role develops current- and future-state architectures, technology roadmaps, data models, governance practices, ETL and data warehouse solutions, and supports digital transformation initiatives. It also provides technical guidance to engineering teams, evaluates technology options, presents architecture recommendations, and partners with business and technical stakeholders in investment management, commercial real estate, and critical infrastructure environments.

Owns governance and end-to-end execution of internal initiatives and strategic programs. Responsibilities include portfolio prioritization, business case evaluation, program planning, risk and dependency management, executive reporting, benefits realization, ROI tracking, and cross-functional coordination. The role establishes accountability, drives initiatives from ideation through adoption, removes roadblocks, and ensures measurable business outcomes across technology, operations, HR, finance, sales, and delivery functions.

Analyze large-scale audience, client, and vendor datasets to assess data quality, matching accuracy, connectivity, and coverage. Use SQL, Python or R, statistical analysis, exploratory analysis, and validation methods to identify anomalies, false positives, false negatives, and improvement opportunities. Create benchmarking and regression datasets, validate audience outputs, report insights, and collaborate with Product, Engineering, and Data Science teams to improve audience data products.