Top Tech Jobs & Startup Jobs

Design and maintain scalable ETL/ELT pipelines, data warehouses, and big data infrastructure. Optimize databases and workflows, implement data quality and governance frameworks, automate testing and deployment, troubleshoot complex data issues, and ensure reliable systems. Collaborate with data scientists, analysts, and business teams to support analytics and machine learning. Mentor junior engineers and establish data engineering best practices.

Manage end-to-end data loading, monitor data feeds, write complex SQL queries, analyze large datasets, perform functional quality control, and support automation, process improvement, liaison activities, and cloud migration. The role requires ownership of operational data processes, rapid issue resolution, rotational shifts, and collaboration throughout the client lifecycle.

Owns the vision, roadmap, delivery, adoption, and value metrics for AI products across Pfizer’s international commercial portfolio. Translates business priorities into technical backlogs and acceptance criteria, coordinates agile delivery, UAT, releases, vendors, and production handoffs, and guides solutions from prototypes to enterprise-grade systems. The role also supports AI prototyping, stakeholder alignment, user engagement, documentation, and integration across data, platform, security, and commercial teams.

Lead the delivery of AI products across international markets by coordinating internal engineers, contractors, and vendors. Establish engineering standards, make technical decisions, review code, ensure testing and release readiness, and guide solutions from proof of concept through enterprise production. Oversee data, AI, generative AI, agentic AI, and full-stack applications while managing dependencies, technical debt, and delivery quality. Collaborate with product, engineering, data science, security, and global stakeholders, and evaluate emerging technologies.

Design and own enterprise-scale GenAI architectures and agentic applications. Lead RAG, embeddings, model integrations, observability, MLOps/LLMOps, security/privacy, and cloud-native deployment using containers and IaC.

Design, test, automate and troubleshoot cloud-native product components within agile cross-functional teams. Ensure test coverage, participate in artifact reviews, document progress for stakeholders, and support continuous improvement and knowledge sharing.

Analyze, develop, and execute integration test cases for network systems. Prepare and validate scripts and configurations, troubleshoot issues, perform integration and migration tasks, and maintain accurate operating system, hardware, software, licensing, and configuration data. The role also involves project scoping, cost and time estimation, process adherence, service delivery, stakeholder management, data security, privacy, and compliance with environmental, occupational health, safety, legal, and financial requirements.

Provides Level 1 and Level 2 technical support for operating systems, applications, hardware, meeting-room technology, and AV systems. Manages ServiceNow tickets, IT asset lifecycles, onboarding equipment, inventory audits, and escalations. Supports company events and town halls through AV setup, rehearsals, live issue resolution, and teardown. Maintains knowledge-base content, contributes to process improvements, and delivers customer-focused service while meeting SLA targets.

Analyze structured and unstructured data, build and maintain Tableau and Adobe Analytics dashboards, perform ad-hoc KPI reporting, validate data quality, generate actionable insights through storytelling, support AI/ML initiatives (NLP, chat transcript and conversational analytics), and automate recurring reports.

Designs and manages advanced data architectures, pipelines, ingestion frameworks, and ETL solutions. Ensures data quality, lineage, accessibility, privacy, and regulatory compliance across AWS, Databricks, Kubernetes, and Teradata platforms. Develops APIs, database views, and data extracts while optimizing storage and processing. Collaborates with cross-functional teams on data sourcing, governance, transformation, and issue resolution. Requires independent judgment and availability for variable schedules, including nights and weekends.

Implements, refines, validates, trains, and deploys machine learning models for products and applications. Designs and monitors data pipelines, evaluates internal and external ML solutions, documents technical requirements and research findings, and develops proof-of-concept strategies. Contributes to patents, APIs, case studies, testing recommendations, and future product research. Requires independent judgment and flexibility to work nights, weekends, and variable schedules.

Processes and fulfills transactional, batch, and self-service data delivery requests for internal and external customers. Transforms and cleanses data, configures scripts, creates and executes test plans, performs quality checks, audits results, and delivers accurate files and reports within service-level agreements. Collaborates with batch production and client engagement teams, reviews technical requirements, and contributes to modernization initiatives.

Develop and maintain high-performance data pipelines using Python, PySpark, and SQL. Transform market, trade, and client data for analytics, reporting, and machine-learning models. Responsibilities include optimizing queries and Spark workloads, testing and reviewing code, maintaining CI/CD pipelines, troubleshooting production issues, documenting data lineage, participating in Agile ceremonies, evaluating technologies, and mentoring junior engineers.

Manage fund accounting deliverables for private equity funds including financial statements, capital calls/distributions, investor allocations, waterfalls, investor and compliance reporting, and daily client interaction. Lead client service teams, improve processes, and handle internal reporting and time tracking.

The Engineer will apply experience in automotive latch commodities or other mechanism commodities within Magna’s Mechatronics, Mirrors and Lighting group. The role supports development of automotive technologies in a dynamic, global environment. The posting provides limited detail on specific daily responsibilities beyond requiring relevant commodity or mechanism experience.

Build, deploy, monitor, and retrain machine learning models for ETA forecasting, status extraction, and logistics automation. Develop regression, classification, time-series, NLP, and LLM-based solutions using noisy supply chain data. Own production ML pipelines, diagnose offline-to-live performance gaps, and measure business impact. Collaborate with product, engineering, and operations teams while mentoring data scientists and making architecture and build-versus-buy decisions.