Data Engineer

• responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem across two independent health plan technology stacks.
• owns the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization), and improves the reliability, observability, and security posture of data pipelines supporting critical Medicaid operations.
• partners closely with the Market BI team as their IT counterpart to improve data access and flow, including establishing and managing Databricks pipeline patterns and platform enablement as the BI environment evolves.
• accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex dual-stack environment.
• primary IT partner for data platform enablement — improving data access patterns, strengthening pipeline governance, and helping establish a maintainable approach to Databricks pipelines and workflows as the market matures its analytics capabilities.

Lead Data Engineer – Modernization, Reliability

• Help clients find answers in their data to impact important missions
• Develop and deploy the pipelines and platforms that organize and make disparate data meaningful
• Work with a multi-disciplinary team of analysts, data engineers, developers, and data consumers
• Support the assessment, design, developing, and maintenance of scalable platforms

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• Own a Data, AI/ML product portfolio spanning Personalization and Marketing
• Define and drive the technical roadmap, balancing innovation, and agile delivery with operational stability and reliability
• Lead the strategic shift from rule-based campaigns to AI and agent-based orchestration
• Oversee full-scale AI/ML & Data pipelines including deep-learning models and optimization capabilities
• Lead architecture reviews, design forums, and technical sessions across Data Science, Engineering, and Technology teams
• Ensure engineering standards are maintained across the portfolio - covering solution design, security, and Data, AI Ops practices
• Drive agile delivery within the Squad model, managing dependencies across a matrixed environment, and removing blockers
• Coach and grow engineering leaders and senior ICs and build a strong internal talent pipeline
• Serve as a trusted partner to Senior stakeholders across Data Science and Business teams
• Translate complex technical concepts into clear, business-relevant narratives for Executive and non-technical audiences

Lead Director – Data Engineering

• Design and implement scalable data pipelines using AWS services including AWS Glue, Step Functions, Lambda, and Kinesis.
• Build ETL/ELT processes to ingest, transform, and load data from various sources into data warehouses and data lakes.
• Architect and maintain data lakes using S3, implement data cataloging with AWS Glue Catalog, and optimize data storage formats (Parquet, Delta, etc.).
• Design data warehouse solutions using Redshift and integrate with existing RDS/Aurora databases managed by the DBA team.
• Develop real-time and batch data processing solutions using Kinesis Data Streams, Kinesis Analytics, EMR, and AWS Batch.
• Create and maintain data models, schemas, and documentation.
• Work with the DBA team to optimize data access patterns and query performance across relational and analytical databases.
• Build automated data quality checks, monitoring, and alerting systems.
• Implement data governance policies and ensure compliance with data retention, privacy, and security requirements.

AWS Data Engineer

• Own the design of data models and structures for analytics purposes; collaborate with our consulting partner and internal team members to bring them to life and iterate based on what actually works in practice.
• Design data ingestion and transformation pipelines that are maintainable, observable, and appropriately documented—not just functional at launch.
• Define the security model for data access, with genuine attention to the intersection of data privacy requirements, compliance obligations, and the usability needs of internal teams.
• Lead tool evaluation and selection across ingestion, data lake, data governance, and data catalog—with a process that produces clear, reasoned recommendations, not just vendor comparisons.
• Ingest priority data sources into our data warehouse or lake and prepare them for analytical use, including validating that the data is trustworthy before downstream teams depend on it.

Data Architect

• Design, document, and maintain Axiom's enterprise data model, ensuring coherence and consistency across disparate systems and platforms.
• Establish and enforce data governance policies, data quality standards, and associated assurance mechanisms to ensure the reliability and integrity of organizational data assets.
• In coordination with the VP, Enterprise Applications, define and maintain the data architecture roadmap, including effort scoping, dependency identification, and alignment with enterprise technology strategy.
• Directly manage the Data Engineer; provide technical mentorship, set priorities, and support professional development.
• Serve as the primary data architecture liaison to the Product, Operations, and Data Science functions.
• In coordination with the VP, Enterprise Applications, evaluate and determine the technology stack for AI solutions and other data initiatives.
• Collaborate with the VP, Enterprise Applications and Integrations Lead to reconcile roadmap priorities and ensure integration-produced data meets enterprise data model standards.
• Participate collaboratively in the design and implementation of Model Context Protocol integrations, enabling AI tooling to access Axiom's information assets.
• Administer and govern the Databricks environment in coordination with the Data Engineer.
• Establish and maintain data documentation standards: entity definitions, lineage, ownership, and access policies.

• Your first day will start with a warm welcome and technical onboarding!
• Your first 30 days will focus on getting to know HackerOne. You will join your new squad and begin onboarding - learn our technology stack (Python, Airflow, Snowflake, DBT, Meltano, Fivetran, Looker, AWS), and meet our Hackeronies.
• Within 60 days, you will deliver impact on a company level with consistent contribution to high-impact, high-performance, scalable source of truth data marts and data products.
• Within 90 days, you will drive the continuous evolution and innovation of data at HackerOne, identifying and leading new initiatives. Additionally, you foster cross-departmental collaboration to enhance these efforts.
• After 90 days, you will deliver impact by developing the roadmap for continuously and iteratively launching high-impact, high-performance, scalable source of truth data marts and data products, and by leading and delivering cross-functional product and technical initiatives.
• Be a technical paragon and cross-functional force multiplier, autonomously determining where to apply focus, contributing at all levels, elevating your squad, and designing solutions to ambiguous business challenges, in a fast-paced early-stage environment.
• Drive continuous evolution and innovation, adoption of emerging technologies, and implementation of industry best practices.
• Champion a higher bar for discoverability, usability, reliability, timeliness, consistency, validity, uniqueness, simplicity, completeness, integrity, security, and compliance of information and insights across the company.
• Provide technical leadership and mentorship, fostering a culture of continuous learning and growth.

Manager, Data Engineering

• Use your expertise to drive data and AI architecture and technology decisions
• Provide insights that influence the overall strategy and plan for a scalable data solution or product that meets business needs
• Educate customers on how to craft highly scalable, flexible, resilient, secure, and cost-effective architectures
• Link technology with measurable business value and outcome
• Identify and investigate new tools and technologies
• Translate client needs into digestible requirements

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes
• Build and optimize data solutions within cloud environments using AWS services and Databricks
• Ingest, transform, and integrate structured and unstructured data from multiple sources
• Develop and maintain data models, data marts, and database structures to support analytics and reporting
• Ensure data quality, integrity, and availability through validation, cleansing, and monitoring processes
• Collaborate with architects, analysts, and developers to translate business requirements into technical solutions
• Support CI/CD processes and automated deployments in collaboration with DevSecOps teams
• Participate in Agile development processes, including sprint planning, backlog grooming, and daily stand-ups
• Troubleshoot data issues, perform root cause analysis, and implement solutions in development and production environments
• Create and maintain technical documentation for data pipelines, transformations, and workflows

• Lead the design, development, and optimization of data pipelines and ETL processes for both structured and unstructured data across the organization
• Define and maintain technical data architecture standards, including integration patterns, platform consolidation, and alignment with emerging technologies (cloud, SaaS, big data)
• Establish and enforce technical components of data governance frameworks, including data quality, metadata management, stewardship, and regulatory compliance
• Drive initiatives for data security, privacy, and adherence to relevant regulations
• Build and mentor high-performing teams across engineering, architecture, and governance disciplines
• Partner with business stakeholders to align data platform initiatives with strategic objectives
• Develop and refine shared data models following industry best practices, for use by stakeholders across the organization
• Lead strategic data platform projects to elevate business understanding, improve operational efficiency, and build data infrastructure and reporting capabilities

Senior Director, Data Platforms

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

Senior Data Engineer

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions
• You are expected to mentor other team members and be a champion of engineering excellence across the organization
• You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution
• You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap
• You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting

Senior Data Engineer, Finance

• Drive the design and implementation of foundational data products and analytics platform capabilities that power Upside’s most critical product and business use cases.
• Lead cross-functional workstreams, shape patterns and architecture across teams, and elevate the overall quality and impact of data work at Upside.
• Design and deliver highly complex, domain-critical data products used by analysts, data scientists, and product teams to unlock new product features, ML models, and strategic decisions.
• Architect scalable, extensible patterns for modeling, orchestration, and data transformation—balancing flexibility, reusability, and cost-efficiency.
• Own platform evolution projects such as migrating the data platform to Snowflake Semantic Views, extending Cortex, or improving orchestration tooling for analytics workflows.
• Lead technical planning and delivery across cross-functional teams, breaking down complex data initiatives into scoped, sequenced workstreams implemented by you and others.
• Drive platform adoption and best practices, mentoring other engineers, building internal documentation and tooling, and raising the overall bar for analytics engineering across the company.
• Influence upstream and downstream teams, partnering with engineering, product, data science, and business stakeholders to align on requirements and deliver end-to-end solutions.
• Represent Analytics Engineering in technical design forums and contribute to roadmap discussions that shape the future of data at Upside.

Staff Data Engineer

• Serve as the expert for ETL and DB solutions, collaborating with business stakeholders and IT teams to define requirements, gather data, and implement optimized data solutions.
• Design, implement, and maintain data systems in Snowflake to ensure data scalability and accessibility.
• Implement and manage data lakes and data warehouses, creating pipelines and data models to enable efficient analytics and reporting.
• Establish and document strategies for managing data transfer processes, including secure file transfers (SFTP), batch data processing, and real-time streaming.
• Build and optimize ETL pipelines for data extraction, transformation, and loading into operational databases or analytical platforms.
• Integrate and support data visualization tools such as Power BI, Sisense, Google Looker, Tableau, or similar platforms to enable actionable insights for business stakeholders.
• Develop and maintain optimized data models for dashboards and reporting, ensuring compatibility with visualization tools.
• Plan, coordinate, and implement database migrations, upgrades, and patches with minimal downtime.
• Define and enforce database governance policies, including data integrity, security, and compliance with regulatory requirements.
• Analyze and resolve database performance issues by optimizing queries, indexes, and schema designs.
• Partner with vendors to evaluate, select, and implement database tools, services, and technologies; stay informed about product roadmaps and industry trends.
• Develop disaster recovery and high-availability solutions, including replication, clustering, and failover.

• Design, construct, and maintain the infrastructure and architecture required for large-scale data processing systems.
• Develop and implement data pipelines, ETL processes, and data integration workflows to collect, transform, and deliver data from diverse data sources.
• Build and maintain scalable data storage solutions, including data lakes, data warehouses, and cloud-based data platforms.
• Collaborate with data scientists, analysts, and application developers to ensure data accessibility and usability for analytics and reporting.
• Optimize data systems for performance, scalability, and reliability.
• Implement data quality checks, validation processes, and monitoring to ensure data integrity.
• Support the development and maintenance of data models and data architectures that align with business and analytical requirements.
• Troubleshoot and resolve issues related to data pipelines, infrastructure, and system performance.
• Maintain documentation for data architecture, workflows, and technical processes.
• Stay current with industry trends, emerging technologies, and best practices in data engineering and data architecture.

• Lead a team of data engineers responsible for designing, building, and maintaining scalable data pipelines
• Collaborate with cross-functional teams to ensure high-quality, reliable data flows
• Monitor project progress and report findings to the leadership team
• Conduct work performance reviews, ensuring adherence to Company standards
• Advocate for data quality, security, and performance improvements

• Own the Data Orchestration roadmap end-to-end across MCP Gateway and ZG Graph, defining clear problem statements, success metrics, and platform adoption targets
• Define and evolve the unified access model for how applications and AI agents interact with data and capabilities, spanning GraphQL, APIs, and AI tool invocation
• Enable Data Mesh adoption by defining and scaling domain-driven access patterns, aligning orchestration with domain models, lifecycle contracts, and cross-domain interoperability
• Establish and uphold the operational excellence bar for the orchestration layer, including availability, latency, observability, and incident response standards
• Define authentication, authorization, and access patterns that enable secure, scalable interaction across data, services, and AI-powered tools
• Design and operationalize governance models for access and orchestration, including lifecycle expectations, versioning standards, and ownership clarity across domains
• Create paved paths and platform primitives that reduce onboarding friction while preventing fragmented or unmanaged integration patterns
• Drive adoption of MCP Gateway and ZG Graph as the default access layer through structured rollouts, enablement, and measurable impact
• Use reliability, adoption, and governance metrics to continuously improve platform performance and prioritize platform hardening versus expansion

Senior Product Manager, Data Platform – Orchestration

• Joining each and every one of your colleagues in creating an inclusive workspace;
• Owning, maintaining, and supporting the reliability, architecture, and evolution of our production machine learning and delivery platform;
• Designing, operating, and improving large scale Databricks/Spark pipelines and workflows
• Partner with our data scientists to productionize new data and ML-powered products;
• Leading improvements on the software integration layers between data pipelines and product workflows;
• Growing the team via mentorship and leadership, reviewing technical documents, improving code quality, and improving team performance and processes;
• Acting as a senior technical owner in debugging complex production issues and improving system observability;
• Collaborating with cross-functional teams to support their data infrastructure needs.

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