Jobs at Anblicks

Lead operational support for SnapLogic and dbt data integration pipelines, including monitoring, incident response, root-cause analysis, SLA management, performance optimization, and release coordination. Manage a small support team, on-call rotations, ticket triage, runbooks, and operational metrics. Partner with client stakeholders and data platform teams to prioritize issues, communicate status, and deliver permanent fixes.

Own production support for SnapLogic and dbt data integration pipelines, including monitoring, incident response, ticket triage, SLA adherence, troubleshooting, and root-cause remediation. Lead support engineers, manage on-call and escalations, maintain documentation, coordinate releases and change management, assess performance, and report operational metrics. Partner directly with clients and data platform teams to prioritize issues, communicate status, and optimize pipelines and transformations.

Build and operate cloud-native Azure data pipelines from source ingestion through Bronze-Silver-Gold transformation layers. Develop Fabric Dataflow Gen2 pipelines, Delta Tables, Synapse SQL evaluation logic, and event-driven integrations using Azure Event Hub and Event Grid. Perform data quality validation, schema reconciliation, lineage verification, and parallel-run testing across multiple domains. Extend configuration-driven frameworks and support sprint-based delivery across concurrent workstreams.

Leads the design, development, and deployment of scalable cloud-native data pipelines for enterprise analytics and reporting. Establishes data engineering standards, governance, security, privacy, and compliance practices. Makes architectural decisions, mentors engineers, conducts reviews, translates business needs into technical solutions, and owns end-to-end data initiatives in an Agile environment.

Designs, develops, and maintains Snowflake-based data solutions, including optimized SQL queries, stored procedures, ETL/ELT pipelines, and data transformations. Performs validation, troubleshooting, performance tuning, and implements data governance, security, and scalability best practices. Collaborates with engineers, analysts, architects, and stakeholders to deliver cloud data warehouse solutions.

Build and scale reusable data modernization assets, including discovery tools, code-generation frameworks, converters, validation patterns, playbooks, runbooks, and estimation aids. Consolidate delivery learnings, drive accelerator adoption across teams, measure reuse, and train delivery pods on accelerators and AI-assisted engineering. Lead modernization programs involving dbt, Snowflake, migration planning, reconciliation, parallel runs, and cutover execution.

Designs, develops, and executes automated UI, API, integration, backend, functional, and performance tests. Responsibilities include expanding automation frameworks and regression coverage, creating testing utilities, validating cloud-based and multi-tier applications, analyzing defects, and supporting CI/CD continuous testing. The role collaborates with developers and QA engineers in Agile/Scrum teams, participates in sprint ceremonies, manages defects in JIRA, and communicates testing progress and risks.

Architect and build enterprise-scale MLOps and AI platforms supporting thousands of production ML models and high-volume distributed workloads. Define architectures for feature stores, MLflow, model registries, distributed training, deployment, inference, monitoring, governance, lineage, explainability, and automated retraining. Establish secure, scalable Databricks Lakehouse standards using Unity Catalog, Delta Lake, DLT, and Mosaic AI. Partner with data teams, optimize reliability and cost, and mentor engineering teams.

Leads enterprise data modernization initiatives by designing scalable cloud data architectures, modernizing pipelines and ETL/ELT frameworks, directing migrations, and guiding engineering teams. Establishes standards for modeling, governance, metadata, lineage, security, reliability, observability, and data quality. Partners with application, cloud, analytics, and business stakeholders to align integrations and data flows. Produces technical designs and migration documentation while supporting testing, production readiness, deployment, and handover activities.

Build and optimize scalable cloud data solutions, including ingestion, transformation, curation, and ETL/ELT pipelines. Apply data modeling and warehousing best practices, 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 CI/CD across Agile and Waterfall environments.

Design and deliver enterprise AI automation solutions across Microsoft 365. Build Copilot agents, Power Platform workflows, RAG-based knowledge systems, MCP server integrations, and AI-to-system connections. Apply prompt engineering and reusable workflow patterns while integrating CRM, ERP, and internal systems. Ensure solutions are secure, compliant, scalable, and governed, collaborating with stakeholders to improve productivity and reduce manual effort.

Lead analysis of large cross-platform TV and streaming datasets for marketing attribution, audience segmentation, media mix modeling, and optimization. Build queries, validate data, define analytical logic, and support scalable data products and automated workflows. Collaborate with engineering, marketing, research, and acquisition teams while maintaining data quality, governance, documentation, privacy, and legal compliance.

Lead strategic assessments of complex enterprise technology landscapes and design future-state architectures, capability models, governance frameworks, and modernization strategies. Develop build-versus-buy recommendations, business cases, cost models, multi-year transformation roadmaps, operating models, and value realization metrics. Partner with executives, business leaders, operations teams, product owners, and technology organizations to align priorities, resolve conflicts, and guide transformation from assessment through execution planning.

Administer and support hybrid IT infrastructure, including Active Directory, Windows and Linux systems, networks, firewalls, VPNs, wireless environments, endpoint security, Office 365, Azure AD, and Intune. Develop PowerShell automation, manage patches, configure Group Policies, troubleshoot DNS and DHCP, and maintain secure, reliable connectivity and device management.

Build reusable data modernization accelerators, frameworks, validation harnesses, and playbook content. Generalize client-specific code for delivery teams, support onboarding, and contribute to migration planning, data validation, reconciliation, parallel runs, and cutover execution across legacy-to-modern cloud platform programs.

Designs enterprise Databricks lakehouse architectures and Delta data models for legacy-platform modernization. Defines migration, validation, reconciliation, parallel-run, and cutover strategies while establishing governance, orchestration, and performance standards. Leads client architecture reviews, guides build teams hands-on, applies AI-assisted conversion approaches, and tunes Spark workloads for scale and cost. The role requires extensive data engineering and architecture experience, including enterprise Databricks implementations and multi-cloud modernization programs.

Designs, develops, and executes automated UI, API, integration, backend, functional, and performance tests. Maintains test plans, cases, frameworks, regression coverage, and testing utilities; analyzes results and defects; and supports manual validation when needed. Collaborates with developers and QA engineers in Agile/Scrum teams, manages defects in JIRA, tests cloud and distributed applications, and integrates automation into CI/CD pipelines.

Lead the design, development, deployment, and optimization of production AI and machine learning systems. Manage end-to-end model lifecycles, collaborate with data science, engineering, and product teams, and ensure model robustness, fairness, and explainability. Mentor junior engineers while contributing to AI engineering practices. Work may include LLMs, RAG, agentic AI, computer vision, real-time inference, edge AI, and MLOps infrastructure.

Identify technical training needs; design and deliver e-learning, workshops, and seminars on cloud, AI applications, and data analytics. Evaluate training effectiveness, manage LMS records, report learning outcomes, support technical onboarding, collaborate with SMEs and vendors, and keep curricula current with technology advancements.

Develop and maintain frontend and backend components of the Hickory application. Build React and NextJS interfaces, backend services, APIs, user management workflows, provisioning automation, data mapping functionality, and cloud integrations. Implement secure, scalable code while working with databases, authentication, authorization, and Azure services.