Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL and Python on BigQuery, Managed Spark, Spanner and AlloyDB.
Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL and Python on BigQuery, Managed Spark, Spanner and AlloyDB.
Stand up working proofs with customers’ data, including de-identifying sensitive data (e.g., PCI, PHI).
Build the data foundation for agentic solutions, including lakehouse architectures, metadata and semantic context, and the access controls that let custom agents act on trusted data.
Train, evaluate and deploy machine learning and agentic components where they improve the customer’s business metric, with evaluation and monitoring in place.
Refactor and migrate high-cost analytics, Spark and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.
Requirements
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience
Lead a Forward Deployed Engineering (FDE) team for Google Cloud’s Data Cloud, bridging the gap between frontier data solutions and production-grade reality for customers.
Serve as the technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
Partner with sales and tech leadership to define requirements for high-value opportunities, deploying specialized experts to key accounts.
Lead technical hiring for FDE, evaluating data engineering expertise, systems engineering, and coding skills to build an exceptional engineering team.
Identify skill gaps in emerging tech (MCP, tool-calling, and foundation models, building agents), ensuring the team maintains subject matter expertise in an evolving data stack.
Senior technical role responsible for the day-to-day development, and delivery of Candid's data pipelines. This role blends deep SQL and ETL engineering expertise with hands-on experience across modern cloud data platform technologies to design, build, and tune pipelines that move data reliably from source systems into Candid's data warehouse and lakehouse.
Compensation
$100,000 - $130,000 (this range is for the NYC area and will be adjusted for other localities; additionally, factors like skills and experience will be considered).
Responsibilities
Architect, develop, and maintain ETL/ELT and C#/.NET pipeline services (SSIS, Airflow, AWS Lambda, enhancer and message-processing services) that move data reliably from source systems into Candid's warehouse and lakehouse.
Support Candid's evolving cloud data platform, including Iceberg ingestion and transformation pipelines, DynamoDB, and query performance tuning via Trino/Starburst.
Monitor data quality end-to-end and investigate production pipeline issues at the root cause, documenting findings and driving fixes through to reprocessing.
Scope and onboard new external and regulatory data sources end-to-end, and contribute to schema and architecture decisions, coordinating changes across ingestion and transformation layers.
Maintain SOP and technical documentation, mentor and review the work of Associate-level engineers, and participate in the team's release and deployment process.
Own meaningful parts of the data platform, including building and scaling ingestion pipelines, driving down latency, modeling the business using dbt, and growing customer data sharing capabilities.
Locations (4)
Toronto, Ontario, Canada
Los Angeles, California, United States
San Francisco, California, United States
Raleigh, North Carolina, United States
Compensation
$120,000 - $197,000 base salary range + annual bonus + stock op
Responsibilities
Build and scale ingestion pipelines from MySQL and Postgres into Snowflake using Airbyte and Streamkap
Responsible for the entire application lifecycle of trading systems, including monitoring, API bridge, and custom integrations, while working with AI coding agents and maintaining production safety.
Responsibilities
Analyze, implement, maintain and operate trading system applications
Participate in on-call and actively monitor and resolve issues
Write specifications, direct AI coding agents, review and test generated code
Ensure strict typing, linting, testing and CI checks
Integrate third‑party trading platforms via Kafka, FastAPI, and database backends
Requirements
Bachelor's degree in Computer Science or related field
Build a new greenfield data platform integrating internal systems (GSCM, SEASI, SMART, and BOSS) using a Medallion Architecture, owning the end-to-end data pipeline from ingestion through Bronze/Silver/Gold transformation, and migrating existing Alteryx workflows to code-based dbt and Python implementations.
Responsibilities
Ingest data from sources such as Microsoft SQL Server (BOSS) and Oracle into the Bronze layer, implementing CDC-based incremental ingestion
Develop dbt models for the Silver layer including cleansing, standardization, deduplication, and data quality testing
Develop Gold layer business-specific data marts and migrate existing Alteryx transformation logic
Configure Apache Iceberg tables and a REST Catalog, including schema evolution and time travel support
Develop and operate Apache Airflow DAGs, managing dependencies, scheduling, and failure notifications
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies, driving complex, cross-functional data initiatives.
Responsibilities
Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
Requirements
Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline
Collaborate with product and engineering to resolve blockers and translate field insights into roadmaps while building internal tools to drive organizational efficiency.
Requirements
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience
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