Industry: Enterprise SaaS, AI-Enabled Technology, Cloud Data Platforms, Data Infrastructure, Analytics Engineering, Machine Learning Enablement, and Digital Transformation
Location: USA Remote
Compensation Package: $208K – $300K
About the Organization
We are a large, enterprise-scale SaaS, AI-enabled technology, cloud data platform, data infrastructure, analytics engineering, machine learning enablement, and digital transformation organization supporting complex digital products, enterprise customers, distributed systems, business intelligence programs, operational analytics, product usage insights, and data-driven decision-making across the United States. The company operates in a high-growth technology environment where data reliability, pipeline performance, governance, scalability, and accessibility directly influence product strategy, revenue decisions, customer success, operational efficiency, AI readiness, and executive visibility.
This is not a traditional data engineering role focused only on writing scripts, moving data between systems, or maintaining basic database jobs. The Data Engineer will serve as a core builder of the company’s modern data foundation, responsible for designing reliable pipelines, improving data architecture, strengthening data quality, supporting analytics teams, enabling business intelligence, and helping prepare the organization for advanced AI and machine learning use cases.
The company is continuing to invest in cloud-native data platforms, scalable ETL and ELT pipelines, data warehouse optimization, real-time and batch processing, data modeling, data governance, observability, self-service analytics, automation, AI-ready data structures, and stronger integration between product, customer, revenue, finance, and operational systems. As the organization grows, leadership is seeking a Data Engineer who can bring strong technical depth, engineering discipline, data quality focus, business awareness, and collaborative problem-solving to a high-impact role.
The Data Engineer will work across Data Analytics, Data Science, Product, Engineering, Finance, Revenue Operations, Customer Success, Marketing, IT, Security, and business stakeholders to build and maintain the data pipelines, models, integrations, and infrastructure that power reporting, analytics, experimentation, customer insights, and operational decision-making. This role will support both technical reliability and business usability, ensuring data is accurate, timely, well-modeled, documented, and trusted.
This role requires a professional who understands that data engineering is not only about infrastructure, but also about creating dependable data products that teams can use with confidence. The ideal candidate will know how to design scalable pipelines, optimize data workflows, troubleshoot failures, improve data models, manage data lineage, support governance, automate manual processes, and partner with analysts and stakeholders to deliver reliable business-ready datasets.
The selected candidate will help improve how the organization collects, transforms, governs, and delivers data across the enterprise. This includes strengthening pipeline reliability, improving warehouse performance, reducing data quality issues, building reusable data models, supporting reporting automation, improving source system integrations, and helping teams move toward a more trusted and scalable data environment.
This is a strong opportunity for a data engineering professional who wants to build modern cloud data infrastructure, support AI-enabled analytics, partner with business and technical teams, and help a large technology-driven organization make faster, more accurate, and more confident decisions through trusted data.
Essential Duties and Responsibilities
• Design, build, maintain, and optimize scalable data pipelines that support analytics, reporting, product insights, revenue operations, customer success, finance, marketing, and enterprise decision-making.
• Develop reliable ETL and ELT workflows that ingest, transform, validate, and deliver data from multiple source systems into cloud data platforms and business intelligence environments.
• Build and maintain data models, curated datasets, semantic layers, reporting tables, and reusable data assets that improve self-service analytics and business visibility.
• Partner with analysts, data scientists, product managers, engineers, and business stakeholders to understand data needs, define requirements, and deliver reliable technical solutions.
• Improve data quality through validation rules, reconciliation checks, anomaly detection, testing frameworks, monitoring, documentation, and root cause analysis.
• Optimize data warehouse performance, query efficiency, pipeline schedules, storage patterns, data refresh processes, and workload management.
• Support integration of source systems such as CRM, finance, product analytics, marketing automation, support tools, HR systems, operational platforms, and internal applications.
• Build and maintain data documentation, including source definitions, data lineage, transformation logic, ownership, refresh schedules, data dictionaries, and metric dependencies.
• Partner with Security, IT, and Governance teams to support access controls, data privacy, data retention, secure data handling, and compliance-aligned data practices.
• Support data observability and reliability by monitoring pipeline failures, latency, data freshness, schema changes, job performance, and downstream reporting impact.
• Contribute to architecture decisions related to data platforms, orchestration tools, transformation frameworks, cloud infrastructure, analytics engineering standards, and AI-ready data foundations.
• Automate manual reporting workflows, recurring data preparation tasks, file-based processes, and business data handoffs where appropriate.
• Support analytics and machine learning readiness by creating clean, reliable, well-structured datasets for modeling, experimentation, forecasting, segmentation, and advanced analytics.
• Troubleshoot data issues quickly and communicate root causes, business impact, timelines, and resolution plans to technical and non-technical stakeholders.
Job Qualifications and Requirements
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Mathematics, Statistics, Data Science, or a related field required.
• Master’s degree or advanced technical education in data engineering, computer science, analytics engineering, cloud platforms, or machine learning infrastructure preferred.
• 4+ years of experience in data engineering, analytics engineering, software engineering, data warehousing, ETL development, cloud data platforms, or business intelligence infrastructure.
• Experience within enterprise SaaS, AI-enabled technology, cloud platforms, fintech, cybersecurity, healthcare technology, digital products, ecommerce, data platforms, or large distributed enterprise environments strongly preferred.
• Strong SQL skills with experience designing efficient queries, transformations, joins, aggregations, data quality checks, and reusable data models.
• Experience building pipelines using Python, SQL, dbt, Airflow, Dagster, Fivetran, Stitch, Matillion, Spark, Kafka, or similar technologies.
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, AWS, Azure, Google Cloud, or similar environments.
• Familiarity with data modeling concepts, dimensional modeling, star schemas, slowly changing dimensions, semantic layers, data marts, and analytics-ready datasets.
• Experience working with business systems and data sources such as Salesforce, HubSpot, Marketo, NetSuite, Workday, Zendesk, Jira, Segment, product databases, event streams, and application logs preferred.
• Strong understanding of data governance, data privacy, access management, data lineage, testing, observability, and documentation best practices.
• Experience supporting BI tools such as Tableau, Power BI, Looker, Mode, Sigma, ThoughtSpot, or similar reporting platforms.
• Ability to work with Data Analytics, Product, Engineering, Finance, Revenue Operations, Customer Success, Marketing, IT, Security, and business stakeholders.
• Strong problem-solving, communication, documentation, technical design, time management, and collaboration skills.
Personal Capabilities and Qualifications
• Technically strong and detail-oriented, with the ability to build reliable data pipelines, validate outputs, troubleshoot issues, and improve system performance.
• Data quality focused, with a strong understanding of how inaccurate, late, or poorly modeled data affects reporting, decisions, customer insight, and business confidence.
• Business-aware and practical, with the ability to understand stakeholder needs and build data solutions that are useful, scalable, and maintainable.
• Collaborative and able to work effectively with analysts, data scientists, engineers, product managers, finance teams, revenue teams, customer success teams, and security stakeholders.
• Strong communicator who can explain data issues, pipeline behavior, technical tradeoffs, and architecture decisions clearly to technical and non-technical audiences.
• Organized and disciplined, with the ability to manage pipeline development, documentation, data requests, platform improvements, incident resolution, and stakeholder priorities.
• Curious and improvement-oriented, with interest in AI-enabled analytics, data automation, cloud platforms, analytics engineering, observability, and modern data architecture.
• Calm and focused under pressure, especially during data outages, dashboard failures, executive reporting deadlines, source system changes, or pipeline incidents.
• Security-aware and responsible when handling customer data, revenue data, employee-related information, product usage data, and confidential business information.
• Strong ownership mindset with the ability to take data problems from investigation through resolution, documentation, and long-term prevention.
Strategic Support
The Data Engineer will provide strategic support by helping the organization build a trusted, scalable, and modern data foundation that supports analytics, AI readiness, customer insight, product decision-making, and operational performance.
This role will help Data Analytics, Product, Engineering, Finance, Revenue Operations, Customer Success, Marketing, and Executive Leadership make better decisions by ensuring data is accurate, accessible, timely, secure, and well-structured. The Data Engineer will ensure data infrastructure is not managed as a collection of disconnected pipelines, but as a reliable enterprise capability that powers reporting, automation, and advanced analytics.
Key areas of strategic support may include:
• Cloud data platform development and optimization.
• ETL and ELT pipeline design, automation, and reliability.
• Data warehouse modeling and analytics engineering support.
• Business intelligence dataset development.
• Data quality, validation, and reconciliation improvement.
• Data observability, monitoring, and pipeline performance management.
• Source system integration and data ingestion.
• Customer, revenue, product, finance, and operational data enablement.
• AI-ready data foundations and machine learning dataset support.
• Data governance, access control, and documentation partnership.
• Reporting automation and self-service analytics support.
• Cross-functional data requirement gathering and technical delivery.
• Data lineage, data dictionary, and metric dependency documentation.
• Continuous improvement of data architecture and engineering standards.
The Data Engineer will help ensure the company has the technical data foundation needed to scale analytics, improve decision quality, and support future AI-enabled business capabilities.
Working Conditions
• Location: USA Remote.
• Primarily remote technical role with regular collaboration across data, analytics, engineering, product, finance, revenue, customer success, marketing, IT, and security teams.
• Flexibility required during data incidents, pipeline failures, source system changes, executive reporting deadlines, platform migrations, product launches, planning cycles, or urgent business data needs.
• Occasional travel may be required for team offsites, architecture workshops, data strategy sessions, company gatherings, or strategic planning meetings.
• Regular collaboration with Data Analytics, Data Science, Product, Engineering, Finance, Revenue Operations, Customer Success, Marketing, IT, Security, and business stakeholders.
• Fast-paced enterprise technology environment with high visibility around data reliability, reporting accuracy, pipeline performance, platform scalability, and analytics enablement.
• Role requires handling confidential customer data, revenue information, financial data, product usage records, employee-related information where applicable, access-controlled datasets, and business performance data with discretion.
Job Function
• Data Engineering
• Cloud Data Platforms
• ETL and ELT Development
• Data Pipeline Architecture
• Data Warehouse Design
• Analytics Engineering
• Data Modeling
• Data Quality Management
• Data Observability
• Source System Integration
• Business Intelligence Enablement
• Data Governance Support
• AI and Machine Learning Data Enablement
• Reporting Automation
• Cross-Functional Data Delivery
Compensation & Benefits
Compensation Package: $208K – $300K
The total compensation package may include base salary, performance bonus, data engineering performance incentives, business impact incentives, long-term incentives, equity participation where applicable, and additional benefits depending on experience, qualifications, and final role alignment.
Benefits may include:
• Comprehensive medical, dental, and vision coverage.
• Performance bonus eligibility.
• Data engineering and business impact incentive opportunities.
• Long-term incentive opportunities.
• Equity or ownership-aligned compensation where applicable.
• Retirement savings plan with company contribution.
• Paid time off and company holidays.
• USA remote work flexibility.
• Visibility across data, product, engineering, finance, revenue, and executive reporting functions.
• Professional development support for cloud data platforms, dbt, Python, data architecture, AI-enabled analytics, machine learning infrastructure, data governance, and engineering education.
• Wellness, employee assistance, and work-life support programs.
• Access to modern cloud data, orchestration, transformation, observability, BI, collaboration, automation, and enterprise technology platforms.
Why Join Us
This is an opportunity to join a large technology-driven organization where data engineering directly influences analytics quality, product insight, customer visibility, AI readiness, operational performance, and executive decision-making.
The Data Engineer will have the opportunity to build modern data pipelines, improve warehouse architecture, strengthen data quality, support business intelligence, and help create a trusted data foundation for a remote-first enterprise.
You will work in an environment that values technical excellence, data reliability, practical problem-solving, documentation, collaboration, and measurable business impact. The company is investing in cloud data platforms, AI-enabled analytics, data observability, automation, governance, and scalable analytics engineering practices.
For a data engineering professional who enjoys building reliable systems, solving complex data problems, enabling analytics teams, and turning raw data into trusted business infrastructure, this role offers the visibility, technical challenge, and platform to make a meaningful impact.