Data Analyst Jobs

WPP is the trusted growth partner for the world’s leading brands. We unite cutting-edge media intelligence and data solutions, world-class creativity, next-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth. We work with the world's most valuable brands and have global reach across 100+ markets, with deep local expertise. Our people are the key to our success. We're committed to fostering a culture of creativity, belonging and continuous learning, attracting and developing the brightest talent, and providing exciting career opportunities that help our people grow. For more information, visit WPP.com. Why we're hiring: As a Data Engineer in the WPP Enterprise Data Group, you will be responsible for the design and implementation of scalable data solutions providing enterprise-scale data transformation across a broad range of projects. Your role will focus on delivering solutions that utilize large-scale data ingestion, processing, storage/querying, streaming, and batch analytics using Databricks . As part of a team, you will implement world-class solutions designed by our data architects. Your responsibilities will include estimating, designing, coding, testing, deploying, and ensuring scalability and performance on Azure using key technologies like Databricks. As a hands-on technologist with an extensive data engineering background using Databricks , you will be joining a group of Data Engineers who are passionate about building the best possible solutions for our business and endorse a culture of life-long learning and collaboration. What you'll be doing: Design, build, test and maintain data pipelines (ELT), according to business and technical requirements. Implement secure pla

WPP is the trusted growth partner for the world’s leading brands. We unite cutting-edge media intelligence and data solutions, world-class creativity, next-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth. We work with the world's most valuable brands and have global reach across 100+ markets, with deep local expertise. Our people are the key to our success. We're committed to fostering a culture of creativity, belonging and continuous learning, attracting and developing the brightest talent, and providing exciting career opportunities that help our people grow. For more information, visit WPP.com. Why we're hiring: The Data Engineer is responsible for the design, development, and implementation of scalable data solutions across the Azure Databricks platform supporting AUNZ. This role focuses on building and maintaining data pipelines using Databricks (Unity Catalog, DABs, DLT), PySpark, and SQL, delivering enterprise-scale data transformation, and supporting Power BI reporting through well-modelled silver/gold layer datasets. The Data Engineer works closely with the Head of Data & Analytics to execute technical scoping and delivery against business requirements. What you'll be doing: KRA 1 Data Pipeline Development • Design, build, test, and maintain data pipelines (ELT) using Databricks, Delta Live Tables, and Databricks Asset Bundles, according to business and technical requirements. • Manage data workflows and orchestration; ensure accuracy and availability of all ELT processes. • Design and deliver silver/gold layer data models within the medallion architecture to support downstream reporting. KRA 2 Platform & Governance • Implemen

WPP is the trusted growth partner for the world’s leading brands. We unite cutting-edge media intelligence and data solutions, world-class creativity, next-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth. We work with the world's most valuable brands and have global reach across 100+ markets, with deep local expertise. Our people are the key to our success. We're committed to fostering a culture of creativity, belonging and continuous learning, attracting and developing the brightest talent, and providing exciting career opportunities that help our people grow. For more information, visit WPP.com. Why we're hiring: As a Data Engineer in the WPP Enterprise Data Group, you will be responsible for the design and implementation of scalable data solutions providing enterprise-scale data transformation across a broad range of projects. Your role will focus on delivering solutions that utilize large-scale data ingestion, processing, storage/querying, streaming, and batch analytics using Databricks . As part of a team, you will implement world-class solutions designed by our data architects. Your responsibilities will include estimating, designing, coding, testing, deploying, and ensuring scalability and performance on Azure using key technologies like Databricks. As a hands-on technologist with an extensive data engineering background using Databricks , you will be joining a group of Data Engineers who are passionate about building the best possible solutions for our business and endorse a culture of life-long learning and collaboration. What you'll be doing: Design, build, test and maintain data pipelines (ELT), according to business and technical requirements. Implement secure platforms

Who We Are Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,400 firms in nearly 60 countries use Addepar to manage and advise on nearly $9 trillion in assets. Its open platform integrates with nearly 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, London, Edinburgh, Pune, Dubai, Geneva, Singapore and São Paulo. The Role We are seeking an experienced and highly motivated Data Engineering Manager to lead Portfolio Data Engineering in Poland, a critical component of the Addepar Platform team. The overall Addepar Platform provides a single source of truth “data fabric” used by the Addepar product set, including a centralised and self-describing repository (a.k.a Data Lake), a set of API-driven data services, an integration pipeline, analytics infrastructure, warehousing solutions, and operating tools. The team has responsibility for all data acquisition, conversion, cleansing, disambiguation, modelling, tooling and infrastructure related to the integration of client portfolio data. Addepar’s core business relies on the ability to quickly and accurately ingest data from a variety of sources, including 3rd party data providers, custodial banks, data APIs, and even direct user input. Portfolio Data integrations and feeds are a highly critical cross-section of this set, allowing our users to get automatically updated and reconciled information on their latest holdings onto the platform. As a Data Engineering Manager, you will play a crucial role in leading and managing our engineering efforts, collaborating closely with product counterparts in an agile envir

Who We Are Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,400 firms in nearly 60 countries use Addepar to manage and advise on nearly $9 trillion in assets. Its open platform integrates with nearly 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, London, Edinburgh, Pune, Dubai, Geneva, Singapore and São Paulo. The Role As a Historical Data Conversion Project Manager, you will serve as the essential link between our technical Data Solution Consultants (DSCs) and the client’s business stakeholders. While DSCs focus on the technical execution of data migration and complex analysis, your role is to translate that work into a clear narrative for the client while relaying client requirements back to the internal team. You will leverage a strong understanding of HDC processes, investment data, and technical frameworks to comfortably bridge the gap between technical execution and client expectations. By working side-by-side with the DSCs to identify data remediation strategies, you will translate technical requirements into actionable directives for the client and proactively secure necessary inputs. This role is for a subject matter expert who can navigate technical language and investment complexities to drive project progress and maintain high-impact communication regarding status, gaps, and timelines. You’ll also be among the first members of the Data Solutions team based in India, helping establish our presence in the region while working closely with colleagues across the U.S., U.K., and other global offices. This is a collaborative role offering the

Opportunity Overview: We are seeking a Senior Data Engineer to contribute to the design and delivery of our cloud-native healthcare data platform. You will implement scalable data solutions built on AWS, Apache Iceberg, Lake Formation, Glue Catalog, Athena, dbt, and modern orchestration frameworks. This role combines strong hands-on engineering with collaboration across platform, analytics, and business teams. What You'll Do Data Engineering Delivery Deliver complex data engineering projects in collaboration with cross-functional teams Drive technical execution from design through production deployment Implement scalable data patterns and reusable frameworks Design and implement batch and near-real-time pipelines Build reusable ingestion, transformation, validation, and publishing frameworks Support modernization of legacy workloads Contribute to Apache Iceberg implementation and optimization Apply standards for schema evolution, partitioning, compaction, and metadata management Ensure efficient storage and query performance Implement data quality frameworks and validation layers Support observability and monitoring practices Contribute to operational excellence and reliability improvements Participate in architecture and design discussions Conduct and participate in code reviews Mentor junior engineers and share best practices ISMS roles and responsibilities Good knowledge of Information security Oversee specific business processes within the ISMS. Responsible to manage the ISMS documentation, conduct risk assessments, and implement risk treatment plans. Risk Owners are responsible for identifying, assessing, and managing risks within their areas of responsibility. They are also responsible for implementing risk treatment plans. Conduct the BCP and other test related to information security continuity along with CISO Responsible for monitoring and reporting on the performance of the ISMS. Responsible for implementation of security policies and procedures and report

Opportunity Overview: We are seeking a Lead Data Engineer to drive the design and delivery of our cloud-native healthcare data platform. You will lead the implementation of scalable data solutions built on AWS, Apache Iceberg, Lake Formation, Glue Catalog, Athena, dbt, and modern orchestration frameworks. This role combines deep hands-on engineering with technical leadership and collaboration across platform, analytics, and business teams. What you’ll do: Lead Data Engineering Initiatives Lead delivery of complex data engineering projects across multiple teams Drive technical execution from design through production deployment Establish scalable implementation patterns Build and Optimize Data Platforms Design and implement batch and near-real-time pipelines Build reusable ingestion, transformation, validation, and publishing frameworks Support modernization of legacy workloads Lakehouse Engineering Lead Apache Iceberg implementation and optimization Define standards for schema evolution, partitioning, compaction, and metadata management Ensure efficient storage and query performance Data Quality and Reliability Implement data quality frameworks Drive observability and monitoring practices Improve operational excellence and reliability Technical Leadership Review architecture and design proposals Conduct code reviews and engineering reviews Mentor engineers and establish best practices ISMS roles and responsibilities: Good knowledge of Information practices. Assist the manager in all the information security activities implementation and maintenance process. Ensuring the team and imparted with Competence related to Information security Responsible for implementation of security policies and procedures and report any issues to the Information Security Manager. Required Qualifications: 8–12 years of Data Engineering experience. Experience leading enterprise-scale data initia

About the Team DoorDash is a data driven organization and relies on timely, accurate and reliable data to drive many business and product decisions. Data is at the foundation of DoorDash success. The Data Engineering team builds database solutions for various use cases including reporting, product analytics, marketing optimization and financial reporting. By implementing data structures and data warehouse architecture, this team serves as the foundation for decision-making at DoorDash. The focus extends to enhancing the developer experience by creating tools that support the organization's high-velocity demands. To lead the growing team of Data engineers we are looking for managers who are passionate about Data and are thought leaders in coaching, guiding and leading teams to make Data a winning edge for DoorDash. About the Role DoorDash is looking for a Data Engineering Manager to guide the development of enterprise-scale data solutions. This manager will also act as a technical expert on all things related to data architecture to empower the greater community of data engineers, data scientists, and DoorDash partners. Your focus extends to fostering an engineering culture of excellence, empowering engineers to deliver reliable, flexible solutions at scale. Additionally, you'll play a pivotal role in building and nurturing a top-performing team, driving innovation and success in a dynamic, fast-paced environment. You must be located in San Francisco, CA, Sunnyvale, CA, or Seattle, WA for this hybrid position. You’re excited about this opportunity because you will… You are a people leader. You thrive in hiring, building, growing and nurturing impactful business focused data teams You are a technology leader. You drive the technical and strategic vision for the embedded pods and foundational enablers to meet current and future needs for scale and interoperability You strive for continuous improvement of data architecture and development process You think of quick wins

Work Experience Required: 8 - 12 Years Experience in programing (Python, R, SQL, NoSQL,Spark) with ML tools & Cloud Technology (AWS, Azure, GCP) Experience in Python libraries such as numpy, pandas, scikit-learn, tensor-flow, scapy, scrapy, BERT etc. Good understanding in statistics, and ability to design statistical hypothesis testing to aid formal decision making. Develops predictive models using Machine Learning algorithms (SVM, Random Forest, Neural Network, Decision Tree, Logistic Regression, K-mean Clustering, linear regression, PCA etc.) Engaging with clients, understanding complex problem statements, and offering solutions in the domains of Retail, Pharma, Banking, Insurance, etc. Contribute to internal product development initiatives related to data science. Develop data science roadmap, and guide data scientist to meet their deliverables. Handling end-to-end client AI & analytics programs. Your role will be a combination of hands-on contribution, technical team management, and client interaction. Proven ability to discover solutions hidden in large datasets and to drive business results with their data-based insights Drive excellent project management required to deliver complex projects, including effort/time estimation. Be proactive, with full ownership of the engagement. Build scalable client engagement level processes for faster turnaround & higher accuracy Define Technology/ Strategy and Roadmap for client accounts, and guides implementation of that strategy within projects Run regular project reviews and audits to ensure that projects are being executed within the guardrails agreed by all stakeholders Manage the team-members, to ensure that the project plan is being adhered to over the course of the project Manage the client stakeholders, and their expectations, with a regular cadence of weekly meetings and status updates. Build a trusted advisor relationship with the IT management at clients and internal accounts leadership. Build

About the Team The Applied organization brings OpenAI’s most advanced technology to the world through products like ChatGPT and the APIs that power a growing ecosystem of developer and enterprise applications. Data Engineering builds and operates the trustworthy, secure, and reliable data systems that power decisions across OpenAI. About the Role We’re looking for a Data Engineering Manager to lead the Growth & Revenue data engineering team. This leader will own the data strategy and execution for the data subject areas spanning growth accounting across all product surfaces, product partnerships, checkout, billing, payments, revenue, and monetization, helping OpenAI understand how people adopt, engage with, and pay for our products. You will partner closely with several Data Science, Business, and Engineering partners to connect product behavior to trustworthy subscriber, payment, and revenue measurement. In this role, you will: Build, manage, and grow a high-performing, inclusive team across the Growth & Revenue data subject areas. Define the data strategy for all the data subject areas you own. Deliver durable, well-modeled data products that connect product behavior, subscription state, checkout events, payment outcomes, and revenue. Establish trusted metric definitions and data quality standards so product, growth, finance, and executive leaders can make fast, consistent decisions. Partner with Data Science and Product teams to support experimentation, causal measurement, funnel analysis, and scalable self-serve analytics. Partner with Finance and Financial Engineering to ensure analytical revenue views reconcile to financial truth and production billing systems. Raise operational excellence for critical pipelines, including reliability, observability, privacy, governance, and incident response. Set a clear roadmap, make principled tradeoffs, and communicate progress and risk across technical and business stakeholders. You might thrive in this role if yo

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE This is a sourcing-first role, not a deal-closing role. Baseten needs someone who can build and maintain deep relationships across the long tail of data center and powered land providers, well beyond the handful of large, well-known players that everyone in the market is already competing for. This coverage area is a key differentiator for Baseten's broader compute strategy, so we're looking for the best possible person in this specific lane rather than a generalist. You'll own the full lifecycle of a sourcing relationship — from first outreach to ongoing management — not just the introduction. WHAT YOU'LL DO Build and maintain a comprehensive map of data center and powered land opportunities, with a particular focus on the long tail rather than the handful of major, oversubscribed players Own the full sourcing lifecycle for each relationship — from identifying and reaching out to new providers, through negotiation support, to ongoing relationship management — not just the initial introduction Develop and manage sourcing relationships across neoclouds, hyperscalers, brokers, and independent operators Quickly and independently evaluate new sites and spaces to determine fit and priority Prepare business cases and cost analysis to support new data center and powered land opportunities, partnering with Finance where needed Maintain accurate records of suppliers, contracts, and commercial terms so the team has a reli

About the Team OpenAI’s Industrial Compute team is building and productizing infrastructure capabilities that help organizations deploy and operate advanced AI systems at scale. The team works across AI hardware, systems engineering, physical infrastructure, and customer delivery to turn emerging technologies into reliable, repeatable infrastructure solutions. Our work sits at the intersection of technical strategy, product development, engineering, and deployment. We partner closely with customers and internal engineering teams to solve complex infrastructure challenges spanning compute, power, cooling, controls, and facility efficiency. About the Role We are seeking a senior, hands-on Data Center Infrastructure Architect to develop and optimize the physical infrastructure required for large-scale AI deployments. This is a broad technical role spanning data center architecture, electrical and mechanical systems, high-density compute, controls, telemetry, and digital modeling. You will use simulation, operational data, and digital-twin approaches to evaluate infrastructure designs, identify system-level constraints, and improve efficiency, reliability, cost, and speed of deployment. The ideal candidate can move fluidly between first-principles analysis, facility and equipment design, computational modeling, engineering review, and real-world implementation. You should be comfortable working across disciplines rather than operating solely within electrical, mechanical, or software boundaries. Key Responsibilities Define system-level architectures for high-density AI data centers across power, cooling, IT equipment, controls, and facility infrastructure. Develop digital twins and other computational models that represent the behavior of data center systems under changing workloads, environmental conditions, equipment configurations, and failure scenarios. Use design and operational data to identify constraints, improve PUE and related efficiency metrics, and optimize

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable. About the Role As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences. We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into defensible evaluation strategies, scalable testing infrastructure, and clear recommendations for technical teams and senior leaders. This role is preferred to be based in San Francisco, CA. In this role, you will: Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring. Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing. Identify the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions

This is where your work makes a difference. At Baxter, we believe every person—regardless of who they are or where they are from—deserves a chance to live a healthy life. It was our founding belief in 1931 and continues to be our guiding principle. We are redefining healthcare delivery to make a greater impact today, tomorrow, and beyond. Our Baxter colleagues are united by our Mission to Save and Sustain Lives. Together, our community is driven by a culture of courage, trust, and collaboration. Every individual is empowered to take ownership and make a meaningful impact. We strive for efficient and effective operations, and we hold each other accountable for delivering exceptional results. Here, you will find more than just a job—you will find purpose and pride. Perform development work and technical support related to our data transformation and ETL jobs in support of a global data warehouse. Can communicate results with internal customers. Requires the ability to work independently, as well as in cooperation with a variety of customers and other technical professionals. What you'll be doing Development of new ETL/data transformation jobs, using PySpark and IBM DataStage in AWS. Enhancement and support on existing ETL/data transformation jobs. Can explain technical solutions and resolutions with internal customers and communicate feedback to the ETL team. Perform technical code reviews for peers moving code into production. Perform and review integration testing before production migrations. Provide high level of technical support, and perform root cause analysis for problems experienced within area of

Job Details: Job Description: As a Material Analysis (MA) Technician, you will be part of a Technology Development (TD) and High-Volume Manufacturing (HVM) lab responsible for performing material analysis and failure analysis in support of Intel's silicon process development and high-volume production. You will work on developing imaging, composition analysis and sample preparation techniques, and best-known methods (BKMs) to improve lab analysis quality, efficiency and output. You will directly interface with TD and HVM fab customers and quality/reliability engineers to develop solutions to problems by utilizing lab capabilities. The scope may include wafer and unit level, front-end modules and back-end/far back-end modules. Responsibilities may include but not be limited to: • Conducting hands-on analysis by effectively utilizing lab techniques, from sample prep micro-cleaver, ion mill etcher, mechanical polish to SEM/EDX, Dual beam FIB, TEM techniques to characterize Si fabricated structures at nanometer scales and integrated circuit device to improve process, performance and reliability; and to identify physical failure mode toward the root-cause identification. • Conducting hands-on data collection with various lab equipment’s, and assisting engineers to implement materials characterization techniques to determine fundamental thin film material structure/properties and to collaborate with process development engineers across functional areas and organizations to improve process performance and reliability. • Supporting and sustaining lab equipment. Ensuring that lab analytical capabilities needed to support advanced transistor and interconnect technology and/or product development are in place. Cooperating with other lab areas beyond local MA/FA (Failure Analysis) labs to achieve