Jobs at Anblicks

Build and maintain scalable data pipelines, ETL/ELT workflows, data models, and integration layers for analytics and AI use cases. Ensure data quality, reliability, and performance; troubleshoot production issues; optimize processing jobs; translate business requirements into technical solutions; and document designs and procedures.

Design, build, and optimize scalable ETL/ELT pipelines and distributed data platforms using Databricks, Apache Spark, Python, Java, and SQL. Develop ingestion, transformation, validation, monitoring, and data quality frameworks for large datasets. Tune Spark jobs and SQL queries, troubleshoot production issues, support cloud-based architectures, and collaborate with cross-functional teams. The role also involves mentoring junior engineers and contributing to engineering standards, governance, security, and operational excellence.

Develop secure, scalable Java applications integrated with Snowflake and the Snowflake Native App Framework. Build Native App packages, manifests, roles, permissions, upgrades, APIs, SQL objects, and Snowpark solutions. Optimize performance and costs, implement testing and CI/CD, integrate cloud and enterprise systems, troubleshoot complex issues, and contribute to architecture, security, code reviews, and technical mentorship.

Build and deploy client-facing solutions using Python, SQL, and modern AI tools. Gather ambiguous requirements, translate business needs into technical implementations, prototype iteratively, troubleshoot issues, and communicate technical concepts to technical and non-technical stakeholders. The role requires strong analytical problem-solving, stakeholder engagement, hands-on SQL, programming proficiency, and practical experience with AI tooling.

Designs and maintains event-driven app-to-app integration pipelines using Azure Event Hubs and Kafka-compatible streaming patterns. Develops production-grade Python producer and consumer applications, integrates Azure services, and troubleshoots distributed real-time systems. The role also establishes event schemas and integration contracts, implements monitoring and alerting, and improves pipeline reliability, throughput, and latency. Familiarity with CI/CD and infrastructure as code supports scalable Azure deployments.

Leads the architecture and delivery of scalable data engineering, AI/ML, and agentic AI solutions. Partners with technical and business stakeholders, oversees implementation quality, conducts code reviews, manages backlogs, timelines, resources, and risks, and coordinates delivery across teams. The role also evaluates emerging technologies, monitors progress, prepares status reports, facilitates meetings, and maintains technical documentation.

Lead enterprise cloud transformation across Azure, AWS, and GCP, including strategy, modernization, governance, FinOps, AI adoption, and multi-cloud architecture. Develop roadmaps, business cases, operating models, and value-realization frameworks while providing hands-on technical leadership across Kubernetes, Infrastructure as Code, DevSecOps, networking, security, observability, and platform engineering. Advise executive stakeholders, lead governance and architecture committees, establish self-service platforms, and mentor technical teams.

Supports a Snowflake platform modernization initiative by building and maintaining data pipelines, transformations, and curated datasets. Assists with data migration, validation, platform readiness, data modeling, and stakeholder collaboration while improving data quality, performance, and maintainability.

Leads technical projects from initiation through completion, managing scope, timelines, budgets, risks, stakeholders, documentation, and deliverables. Collaborates with developers, engineers, and designers to ensure project alignment and quality. The role requires technical project management experience, prior software engineering experience, familiarity with project management tools, and knowledge of Agile and Scrum methodologies.

Lead production support and continuous improvement for an Azure and Databricks data platform. Responsibilities include incident resolution, root-cause analysis, stakeholder communication, monitoring, performance and cost optimization, data quality and governance, change and release management, KPI reporting, documentation, training, and automation including self-healing capabilities.

Design and build Go-based integration services, APIs, and event-driven workflows using AWS SQS, SNS, Lambda, EKS, Kubernetes, and RDS. Develop resilient, observable, well-tested services with strong API contracts, schema evolution, and enterprise integration patterns. Contribute to containerization, CI/CD, performance tuning, and non-functional validation. Use AI-assisted development responsibly, review code, mentor developers, and provide technical guidance.

Designs and maintains scalable data pipelines, develops dbt transformation workflows, and architects Snowflake data models and warehousing solutions. Integrates multiple data sources, implements quality monitoring, troubleshoots pipeline issues, optimizes SQL and warehouse performance, documents architecture, and collaborates with analysts, data scientists, and stakeholders to deliver reliable analytics infrastructure.

Leads Snowflake architecture, administration, governance, performance optimization, security, replication, disaster recovery, and operational reliability. Supports Oracle and other database technologies across cloud environments, handles incidents, patching, upgrades, and troubleshooting, and automates administration and monitoring. Coordinates with IT, developers, and vendors, improves platform scalability and standardization, and provides on-call support.

Build and optimize scalable cloud data solutions, including ingestion, transformation, curation, and ELT/ETL pipelines. Develop data models and warehouses, automate workflows, monitor pipeline health, troubleshoot quality and performance issues, and optimize reliability and cost. Collaborate with Cloud Solution Architects, lead code reviews, follow controlled development procedures, and support Agile and Waterfall methodologies.

Develop and deploy machine learning and statistical models for complex business problems. Responsibilities include data exploration, feature engineering, model training, validation, tuning, production deployment, pipeline collaboration, performance monitoring, MLOps, and communicating insights to technical and business stakeholders.

Lead end-to-end delivery of large-scale data engineering, Databricks, analytics, cloud data, and AdTech transformation programs. Manage scope, roadmaps, budgets, resources, risks, milestones, and distributed technical teams. Provide technical oversight of lakehouse architectures, data platforms, governance, and optimization. Coordinate engineering, analytics, AI/ML, DevOps, product, and business stakeholders while driving Agile, SDLC, quality, and delivery governance. Serve as the primary technical and executive client interface.

Design, develop, and maintain scalable automation frameworks for web applications, APIs, and end-to-end workflows. Build API automation, integrate testing with CI/CD pipelines, and apply Generative AI, AI Agents, prompt engineering, and intelligent analysis to improve test coverage and quality. Collaborate across the SDLC, evaluate emerging automation technologies, and lead automation initiatives from design through implementation.

Design, develop, test, and support scalable cloud-native applications for enterprise data and analytics platforms. Build backend services with Java and Spring Boot, responsive frontends with Angular, RESTful APIs, and microservices. Deploy applications on AWS using Docker and Kubernetes, automate CI/CD with Jenkins, and implement secure data collaboration with AWS and Snowflake Clean Rooms. Participate in troubleshooting, optimization, code reviews, architecture discussions, and collaborative GitHub workflows.

Designs, develops, and optimizes Snowflake and dbt data warehouses, ELT/ETL pipelines, dimensional models, and data quality frameworks. Builds reusable dbt models, tests, snapshots, documentation, and CI/CD workflows. Ensures data governance, security, compliance, metadata management, and lineage while troubleshooting data issues. Collaborates with analysts, data scientists, product teams, and stakeholders, documents architectures, and mentors junior engineers.

Build and evaluate predictive models for complex transactional data, using advanced SQL, Python, cloud data infrastructure, and statistical methods. Trace data quality issues through ETL and CDC pipelines, communicate findings to senior leaders and nontechnical stakeholders, and use AI tools to accelerate analysis. Preferred work includes LLM, RAG, BI, data testing, vector database, and commercial real estate applications.