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Data Engineering & Pipeline DevelopmentDesign, develop, and maintain scalable ETL/ELT pipelines to support enterprise data integration and analytics.

A highly collaborative team player who enjoys solving complex data problems • Passionate about building scalable and reliable systems

, and designing scalable data lake and data warehouse solutions. Experience across multiple data platforms (Databricks, Snowflake, Azure Data Factory, Synapse, etc.) is a strong advantage. Key Responsibilities 1.

Lead the design and implementation of complex data ingestion pipelines using PySpark, Databricks Intelligence Platform, and AWS services (S3, IAM roles and policies), including Kafka/streaming architectures, schema evolution

Design, develop, and maintain scalable data pipelines.

Design & Build Pipelines: Design, build, and maintain robust, scalable batch and streaming data processing, storage, and integration pipelines.

Design, build, and maintain scalable and reliable batch and real-time ETL/ELT data pipelines using cloud services such as GCP Dataflow, Cloud Functions, Pub/Sub, and Cloud Composer.

Design, build, and maintain scalable data pipelines across diverse environments.

Partner with technical and non-technical stakeholders to translate business requirements into scalable, reliable, and maintainable data engineering solutions.

Regularly design, develop, and maintain data pipelines that support operational, analytical, and machine learning workloads

Design and build scalable, well-tested DBT models.

Connect with cross-functional teams: The data environment we’re building will be used by members of the Accounting, Finance, Supply Chain, and Publishing teams, among others.

Build and maintain scalable data services, pipelines and storage solutions for the feedback of unstructured application data for ML training and evaluation purposes.

Design, build, and maintain scalable data pipelines for analytics, ecommerce, logistics, and marketing.

You want to impact the industries that run our world: Your efforts will result in real-world impact—helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly

Put Patients First

Collaborate with product managers, data analysts, and machine learning engineers to develop pipelines and ETL tasks in order to facilitate the extraction of insights.

Guide clients on optimizing their data environment to work most effectively and efficiently for them. Clarify management objectives through data solutions.

Design, develop, and maintain scalable data pipelines using modern distributed data processing platforms and cloud environments.

Support partner onboarding into clean room environments across platforms such as Snowflake, LiveRamp, Databricks, or similar technologies.

Designs and develops moderately complex queries, objects, ETL packages, reporting structures, and other objects necessary to support detailed analysis and self-service reporting needs.

Lead the creation and optimization of existing data models through AI code generation on eventing, customer level, and process level data.

You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact – helping to keep the lights on, get food into grocery stores, and most importantly