Data Engineer

• Build and refine a state-of-the-art cloud data platform powering AI and analytics
• Enable the creation of advanced data products for sophisticated consumers
• Deliver value as part of an agile data engineering team using modern tools and technologies
• Work closely with the data architect and stakeholders
• Help MAPFRE USA transform into a data-driven organization
• Automate infrastructure provisioning
• Integrate security into CI/CD pipelines using DevSecOps practices
• Conduct threat modeling, vulnerability assessments, and incident response
• Monitor and log cloud infrastructure and systems
• Optimize system performance and cloud costs
• Collaborate across Development, Operations, and Security teams
• Support ingestion, transformation, and orchestration of data

Data Platform Engineer II/III

• Design, build, and maintain scalable and secure data architecture in Snowflake
• Develop and optimize Snowflake objects, including tables, dynamic tables, views, and warehouses
• Manage Snowflake security and access
• Partner with the Integration team on ETL pipelines in and out of the data warehouse using Snowflake-native tools, Informatica, and MuleSoft
• Build and manage deployment of Snowflake changes across environments, including production, using GitHub
• Ensure data quality, integrity, and lineage across pipelines
• Write and optimize query performance and warehouse usage
• Monitor Snowflake compute costs
• Develop scripts for moving data and ETL/ELT between AWS and Snowflake
• Work with AWS, S3, Azure, GCP, and blob storage
• Support the production Enterprise Data Warehouse environment, including ingestion and transformation issues
• Perform unit testing and clearly document builds
• Partner with data analysts, governance, and Business Intelligence teams to enable analytics
• Support the Data Architect and Business Intelligence teams with optimized data models
• Translate business requirements into scalable data solutions
• Maintain working knowledge of the organization’s mission and programs
• Perform other related duties as assigned

• Build a complete inventory of historical data sources, including location, client, project, site/program, equipment unit, date range, format, and completeness
• Write format specifications for common record types, with example files
• Transcribe test files into a consistent tabular template and save them to a designated staging area
• Produce data quality reports covering gaps, incomplete or suspect analyses, and contradictions between files
• Conduct exploratory data analysis to identify candidate correlations across datasets
• Write feasibility memos on which questions the historical data can and cannot support
• Apply file naming/tagging conventions to legacy folders as needed
• Prepare metadata for a document-system migration as needed
• Cross-reference test records against related measurement datasets as needed

Data Engineering, ML Intern

• Define and champion the architectural roadmap and best practices for end-to-end data pipelines, ensuring scalability, reliability, and security
• Mentor engineers, conduct code reviews, and drive project timelines for rapid delivery of data products
• Partner with Data Scientists, Analysts, and business stakeholders to translate requirements into production-ready data solutions
• Collaborate with Data Scientists and ML Engineers on data accessibility, model development support, and data quality assurance
• Design, build, and optimize high-volume data ingestion and transformation jobs using dbt Core and AWS Glue
• Develop and maintain modular, reusable data pipelines using orchestrators such as Dagster
• Implement and manage real-time data flows using Confluent platforms or native AWS streaming services such as Kinesis
• Implement data anonymization and comply with data security and privacy regulations
• Implement and maintain the Iceberg open table format for schema evolution and data management
• Optimize query performance and cost efficiency across Snowflake, Amazon Redshift, and AWS Athena
• Integrate monitoring and observability into pipelines using Splunk to identify bottlenecks and troubleshoot production issues

• Perform data analysis and assess data management requirements for platforms or journeys
• Maintain expert knowledge of upstream data through data profiling, data quality reporting, and metadata production
• Support data acquisition and ingestion
• Design and develop data models, including entity relationship diagrams and dimensional data models
• Develop and maintain complex ETL jobs and frameworks using the Bank's standard tools
• Elicit, analyze, and understand business and data requirements to develop complete business solutions
• Ensure data complies with enterprise data standards, policies, and guidelines
• Provide development and testing teams with support to resolve data issues, including escalation support for complex issues
• Support partners and stakeholders in interpreting and analyzing data
• Consult with peers and partners across multiple teams on research, analysis, design, and support
• Coordinate with technology work teams such as ITS, ARE, Architecture, and Enterprise Protect to ensure delivery success
• Support QA with data analysis and investigations during SIT/UAT/PAT testing
• Provide oversight on post-implementation activities during the warranty period
• Execute and approve code check-in/check-out into source code repositories
• Support code packaging and CI/CD deployment into higher environments
• Lead participation in application design and architecture reviews
• Raise ServiceNow requests and support release management activities
• Lead data engineering initiatives and data governance capabilities across the organization
• Ensure metadata and data lineage are captured and compatible with enterprise tools and processes
• Adhere to secure coding practices and identify technical decisions, risks, lessons learned, and enhancements
• Protect organizational interests by identifying, managing, and escalating risks
• Keep current on emerging trends, business knowledge, tools, and techniques
• Enable, mentor, and provide guidance to team members through knowledge sharing and engineering best practices
• Maintain effective working relationships with internal and external business partners and stakeholders
• Keep stakeholders informed of project status and day-to-day activities
• Contribute to a fair, positive, equitable, and diverse work environment
• Act as a brand ambassador for TD and the relevant business area

Data Engineer III

• Design, build, and optimize data models on SAP HANA Cloud, including tables, calculation views, CDS artifacts, stored procedures, and SQLScript
• Design and implement graph data models and traversal queries for connected-data problems including material substitution and compatibility, product and organizational hierarchies, and supply and fulfillment networks
• Write and tune high-performance SQL and SQLScript by analyzing execution plans and resolving bottlenecks
• Assist with performance engineering across the data layer, including partitioning, indexing, memory management, workload classes, and result caching
• Build data ingestion and replication into HANA Cloud from SAP and non-SAP sources using SDI, SDA, SLT, change data capture, or comparable approaches
• Expose data to the TypeScript product stack through clean, secure, and documented service layers
• Establish and enforce standards for schema design, migrations, versioning, and database-artifact code review
• Implement security and access control, including roles and privileges, data masking and anonymization, and multi-tenant data isolation
• Collaborate with machine learning and search engineers to support feature pipelines and search indexing
• Own monitoring, alerting, backup and recovery, capacity planning, and HANA Cloud upgrade readiness
• Document data models and graph schemas for engineering teams

Senior Data Engineer – SAP HANA Cloud, Graph

• Own Fullbay's data platform end to end, including architecture, hands-on development, and company-wide data standards
• Own certified metric and entity definitions and serve as final arbiter of cross-functional definitional disagreements
• Keep documentation current so business users can find trusted answers without assistance
• Build and extend the gold layer using SQL and transformation code in version control with automated tests
• Own identity resolution across Classic, Next, and Salesforce, including crosswalk logic, survivorship rules, and platform-of-record determination
• Own Snowflake role hierarchy, grants, row access and masking policies, warehouse sizing, cost attribution, and spend
• Design curated access paths for AI agents, including per-agent identities and grants, registration patterns, and retention policies
• Onboard new data sources, own schema drift and deletion detection, and partner with engineering on source schema changes
• Scope and direct the retained architecture consultant's work, review deliverables, and reject nonconforming work
• Provide guidance and office hours for business users and AI builders self-serving against the data platform
• Adhere to confidentiality and compliance regulations
• Perform other duties as assigned

Lead Data Engineer

• Develop scalable, secure data pipelines from on-premise systems of record to Google Cloud Platform services
• Leverage and extend roadmaps for reusable ingestion, transformation, quality, and orchestration frameworks and tooling
• Enable self-service data consumption and governance through standardized patterns, templates, and sandbox capabilities
• Support training, validation, and monitoring use cases using BigQuery, Dataflow/Apache Beam, Dataproc/Spark, Pub/Sub, and Cloud Storage
• Create standardized feature transformation pipelines and a common feature store with lineage, dictionary, and high availability
• Ensure cost, performance, and reliability of GCP data workloads through partitioning, clustering, storage classes, and autoscaling strategies
• Develop transformation libraries in Python, SQL, and Beam
• Provide orchestration through Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration
• Implement data modeling and semantic layers using dimensional, data vault, or canonical models with BigQuery or similar tools
• Enforce data quality, lineage, and observability through standardized metrics, validation rules, and monitoring dashboards
• Partner with data scientists and domain solution teams to migrate existing models onto GCP capabilities
• Document patterns, runbooks, and best practices; provide enablement through workshops and code examples

Senior Data Engineer

• Own the data roadmap and company data model, establishing a single source of truth for core business data
• Refactor legacy jobs and technical debt so the platform scales with the business
• Maintain resilient pipelines and trusted, accurate, available data
• Manage warehouse and infrastructure costs as data volume grows
• Run intake and sprint cadence, prioritizing requests by business impact
• Lead and coach a small team through code reviews, feedback, and growth paths
• Connect enterprise systems such as ERP into the warehouse
• Make the team AI-native by using AI agents for development, incident investigation, and data quality
• Build an enterprise-level data function with cleaner models and reduced technical debt

Director of Data Engineering

• Validate HIPAA X12 transaction sets — 837 (I/P/D), 835, 834, 270/271, and 276/277 — to confirm claims, eligibility, and remittance data moves correctly between systems
• Diagnose transaction-level errors in loops, segments, elements, and qualifiers
• Partner with development teams to fix mapping or programming issues
• Investigate and resolve trading partner and vendor transaction problems in production
• Create test plans, test cases, and Companion Guide documentation
• Lead UAT, SIT, and end-to-end testing for vendor onboarding, system enhancements, and HIPAA version upgrades (4010/5010)
• Build and maintain mock 837I/837P claim test data
• Coordinate between trading partners, clients, and internal technical and non-technical teams
• Gather requirements and close the loop on issue resolution
• Log incidents and enhancement requests in JIRA
• Maintain project documentation in Confluence
• Validate ICD-9/ICD-10 and HCPCS coding during claims and EDI testing
• Participate in Agile sprint planning and sprint review

Data Engineer jobs in United States

Data Platform Engineer II/III

Data Engineer

Data Engineering, ML Intern

Data Engineer

Data Engineer III

Senior Data Engineer – SAP HANA Cloud, Graph

Lead Data Engineer

Senior Data Engineer

Director of Data Engineering

Data Engineer, EDI