Top Data & Analytics Jobs

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Design secure, resilient, and reusable data engineering solutions; support distributed and streaming workloads; enforce engineering standards; collaborate with product, software, analytics, and data science teams; communicate technical concepts; and mentor other engineers.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Develops data solutions, dashboards, advanced analytics, automation, and data-quality monitoring to support risk management and business decision-making. Partners with business and technology teams on data visualization, governance, metadata, lineage, access controls, and self-service analytics. Requires quantitative education, data analytics experience, scripting, SQL or related data platforms, business intelligence tools, and AWS exposure. The position is hybrid in Alabang with a mid-shift schedule.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists to deliver data solutions. Develop distributed and real-time workloads, enforce reusable engineering patterns, apply security controls, and optimize reliability and performance. Mentor data professionals, communicate technical concepts to stakeholders, and independently architect end-to-end solutions using relational and NoSQL databases, cloud warehouses, and modern data engineering technologies.

Leads data engineering architecture and modeling initiatives, designing scalable pipelines, cloud data platforms, and reusable data solutions. Collaborates with engineering, product, analytics, and data science teams while mentoring developers and establishing common design patterns. Responsibilities include distributed data processing, relational and NoSQL systems, data security, observability, streaming pipelines, and operational excellence across platforms such as Databricks and Snowflake.

Leads data engineering teams and technical strategy for scalable cloud data platforms. Oversees architecture, pipelines, data models, quality, observability, and operational standards across Databricks, Spark, Python, and Snowflake environments. Mentors engineers, drives large-scale data initiatives, evaluates platform choices, and collaborates with product and software teams to deliver reliable data solutions.

Lead product analytics for Enterprise Data products, validating analytical tools, assessing methodology risks, researching emerging practices, and translating complex data into actionable insights. Manage validation projects, communicate findings through reports and presentations, and coach junior staff. The role requires advanced data analysis, statistical or econometric modeling, predictive analytics, large-dataset analysis, data governance, and people leadership.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while applying Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and resilient architectures. Mentor data professionals, communicate technical concepts to stakeholders, and deliver user-centric data solutions with minimal supervision.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role requires distributed data engineering, data modeling, relational and NoSQL databases, security controls, and cloud data warehousing. Responsibilities include influencing cross-functional technical teams, enforcing engineering patterns, supporting resilient and efficient data solutions, collaborating with product and software engineering teams, communicating technical concepts, mentoring data professionals, and staying current with emerging technologies.