fivetran jobs in San Diego

Staff Data Engineer at Qualcomm responsible for designing, building, and maintaining scalable batch and streaming data pipelines on AWS and Databricks. Develop reusable data engineering frameworks, libraries, and templates while implementing AI-driven automation for schema inference, data quality checks, pipeline orchestration, and self-healing mechanisms.…See more Lead reliability and operations efforts by defining SLIs/SLOs, participating in incident response, and driving performance tuning and cost optimization. Mentor junior engineers, collaborate across analytics and platform teams, and drive adoption of standardized data frameworks and best practices.

Lead hands-on modernization of legacy SSIS/SSRS ETL workloads into cloud-native Lakehouse pipelines using Databricks, dbt, Fivetran, and Airflow. Design and enforce standardized data frameworks (ingestion, transformation, curation, consumption), drive CI/CD and observability practices, and mentor engineers on modern data engineering patterns.…See more Evaluate emerging technologies (Delta Live Tables, Iceberg, streaming, AI-driven observability) through POCs and POVs, and leverage AI-assisted tools (Databricks Assistant, GitHub Copilot, Cursor AI) to accelerate development and reduce technical debt. Optimize Spark workloads and orchestration across Azure and GCP environments while partnering with architects, platform engineers, and business stakeholders.

Lead a Data Engineering team to establish modern DataOps and CI/CD practices, mentor engineers in PySpark, SQL, dbt, Fivetran, and cloud-native development, and design scalable data infrastructure across GCP, Azure, and AWS cloud data warehouses (BigQuery, Snowflake, Databricks). Manage technical delivery, collaborate across enterprise teams to develop end-to-end data solutions, and align data initiatives with business goals while remaining current on industry trends.See more

Lead the modernization of legacy SSIS/SSRS data systems into cloud-native Lakehouse pipelines using Databricks, dbt, Fivetran, and Airflow. Design standardized frameworks for ingestion, transformation, and consumption layers while driving CI/CD, observability, and engineering best practices across Azure and GCP.…See more Evaluate emerging technologies (Delta Live Tables, Iceberg, streaming ingestion) through POCs and POVs, and leverage AI-assisted tools (Databricks Assistant, Cursor AI, GitHub Copilot) to accelerate development and reduce technical debt. Provide technical guidance to the team and translate business requirements into scalable data solutions.

Lead a Data Engineering team to establish modern DataOps and CI/CD practices while providing technical mentorship in PySpark, SQL, dbt, and Fivetran. Design and optimize scalable, reliable data infrastructure across cloud and on-premise ecosystems (AWS, GCP, Azure) using Databricks, BigQuery, and Snowflake.…See more Manage end-to-end data sourcing, processing, integration, and delivery projects to meet business objectives and predetermined success criteria. Require 10+ years managing Data Engineering teams and 7+ years working with cloud-native data platforms and advanced SQL/Python/PySpark expertise.

This Data Engineer II role focuses on data operations and infrastructure, supporting the transition of data systems from development to production. The engineer will administer data lakehouse/warehouse platforms (Snowflake, Databricks, Redshift, BigQuery, Azure Synapse), manage RBAC and user governance, monitor and troubleshoot data pipelines, and contribute to data governance and quality best practices.…See more Key responsibilities include orchestration tool administration (Apache Airflow, dbt), observability framework development, incident management, and hands-on SQL and Python work to maintain and optimize production data systems.

Design, build, and maintain scalable batch and streaming data pipelines on AWS and Databricks, developing reusable data engineering frameworks and standardized patterns for ingestion, transformation, and validation. Leverage AI-based techniques to automate data quality checks, anomaly detection, and pipeline self-healing.…See more Define SLIs/SLOs, participate in on-call rotations, and mentor junior engineers while driving adoption of data frameworks and best practices across analytics, reporting, and AI/ML teams.

Design, build, and maintain data pipelines that ingest, transform, and load data from SQL Server, PostgreSQL, SaaS platforms like HubSpot, and internal applications into Snowflake. Develop and optimize data models, manage data quality and governance, and create dashboards and reports to support reporting and analytics use cases.…See more Collaborate with business stakeholders to gather requirements and translate them into scalable data solutions. Optimize query performance and storage efficiency within Snowflake while implementing best practices in documentation and pipeline monitoring.

Design and build scalable, distributed data platform systems on AWS and Databricks, owning workspace architecture, governance, and lifecycle management. Implement infrastructure as code with Terraform, develop end-to-end data pipelines from ingestion to analytics and ML, and operate Amazon EKS clusters.…See more Define SLIs/SLOs for platform reliability, lead incident response and on-call rotations, implement security and compliance controls, and mentor engineers on platform standards. This senior staff role requires 8+ years building large-scale cloud platforms, 5+ years in Data Platform/DevOps/SRE roles, expert AWS knowledge, and deep hands-on experience with Databricks Lakehouse (Unity Catalog, Delta Lake, MLflow), Terraform, CI/CD pipelines, and Python/Bash scripting.

Design, build, and maintain data pipelines that ingest, transform, and load data from SQL Server, PostgreSQL, SaaS platforms (HubSpot), and internal applications into Snowflake. Develop and optimize data models, manage data ingestion using Azure Data Factory and data replication tools, and ensure data quality and integrity.…See more Collaborate with business stakeholders to translate analytics requirements into scalable solutions, develop dashboards and reports, and optimize query performance. Implement data governance best practices, monitor pipelines, and recommend technology improvements to support the company's data strategy.

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace.…See more Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.