We are a dynamic and rapidly expanding enterprise technology organization operating in a data-driven environment where reliable data pipelines, scalable cloud platforms, accurate reporting, analytics readiness, and intelligent automation directly influence business performance. Our organization supports enterprise customers, product teams, business operations, data analytics groups, AI initiatives, finance functions, customer success teams, and executive decision-making processes that require strong data architecture, disciplined engineering, and trusted information flow.
As companies grow, data engineering becomes one of the most important foundations for operational visibility, product innovation, customer intelligence, financial planning, machine learning, and enterprise decision-making. Modern organizations need data engineers who can do more than move data from one system to another. They need professionals who can design reliable pipelines, improve data quality, manage cloud data infrastructure, support analytics teams, enable AI use cases, and ensure data is available, accurate, secure, and usable across the business.
Our company is investing heavily in cloud data platforms, AI-enabled data workflows, data warehouse modernization, data lake architecture, analytics engineering, pipeline automation, data governance, real-time data processing, machine learning enablement, business intelligence, data quality monitoring, and scalable enterprise reporting. We are seeking a highly capable Data Engineer who can build and improve the data systems that power analytics, product insights, customer intelligence, and business operations.
This is not a traditional data engineering role focused only on routine ETL jobs, basic database maintenance, or isolated reporting requests. The Data Engineer will serve as a technical data partner responsible for building scalable data pipelines, improving data models, integrating business systems, strengthening data reliability, supporting analytics and AI initiatives, and helping the organization turn complex data into trusted business value.
The selected candidate will work closely with Data Analytics, Business Intelligence, Product, Engineering, Machine Learning, Finance, Customer Success, Revenue Operations, Security, IT, and Executive Leadership to ensure data platforms are accurate, secure, scalable, and aligned with business priorities. This role will support both foundational data infrastructure and high-impact business initiatives where data quality and timely access are critical.
This is a strong opportunity for a data engineering professional who wants to work with modern cloud platforms, support enterprise-scale analytics, enable AI-driven business capabilities, and help build a more mature, trusted, and scalable data ecosystem for a remote-first organization.
As Data Engineer, you will design, build, test, deploy, and maintain data pipelines, data models, data integrations, warehouse structures, transformation workflows, and cloud-based data systems.
You will be responsible for moving, transforming, validating, organizing, and optimizing data from multiple source systems so business teams, analytics teams, product teams, and leadership can access reliable and actionable information.
You will serve as a trusted technical partner across data and business teams while maintaining accountability for data quality, pipeline reliability, platform performance, documentation, security, and long-term scalability.
The ideal candidate combines strong engineering fundamentals, SQL expertise, cloud data platform experience, data modeling discipline, problem-solving ability, and strong communication to support measurable business outcomes.
Design, develop, test, deploy, and maintain scalable data pipelines, ETL and ELT workflows, batch processing jobs, streaming data flows, data warehouse tables, and analytics-ready datasets.
Integrate data from enterprise systems such as CRM, ERP, product platforms, customer success tools, finance systems, marketing systems, operational tools, and third-party data sources.
Build and optimize data models that support business intelligence, executive reporting, customer analytics, product analytics, revenue operations, finance reporting, and operational decision-making.
Partner with Data Analytics and Business Intelligence teams to understand reporting needs, define data requirements, improve metric logic, and deliver trusted datasets.
Partner with Product, Engineering, and Machine Learning teams to support product telemetry, event tracking, feature usage data, model training datasets, AI workflows, and data-driven product capabilities.
Improve data quality through validation rules, reconciliation checks, anomaly detection, monitoring, testing, lineage documentation, and issue resolution processes.
Develop and maintain transformation logic using SQL, Python, dbt, Spark, Airflow, or similar tools depending on platform needs.
Support cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, or similar modern data environments.
Optimize pipeline performance, warehouse cost, query speed, storage efficiency, data freshness, error handling, and operational reliability.
Create and maintain technical documentation, data dictionaries, transformation logic, lineage notes, source-to-target mappings, runbooks, and operational procedures.
Partner with Security, Compliance, and IT teams to support data access controls, privacy requirements, role-based permissions, secure data handling, and audit readiness.
Troubleshoot pipeline failures, data discrepancies, performance issues, schema changes, broken reports, and system integration problems.
Support data governance by helping standardize definitions, improve documentation, manage data ownership, and strengthen trust in key business metrics.
Contribute to data platform modernization by identifying manual processes, unreliable workflows, data silos, legacy dependencies, and opportunities for automation.
5+ years of experience in data engineering, analytics engineering, database development, ETL development, cloud data platforms, business intelligence engineering, or data infrastructure.
Experience working within enterprise SaaS, AI-enabled technology, cloud platforms, fintech, healthcare technology, cybersecurity, ecommerce, business services, digital products, or large-scale data environments preferred.
Strong SQL skills with experience writing complex queries, joins, window functions, stored procedures, performance tuning, data validation logic, and transformation workflows.
Experience with programming or scripting languages such as Python, Scala, Java, or similar technologies used for data processing, automation, or pipeline development.
Experience with modern data platforms such as Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, PostgreSQL, SQL Server, Oracle, or similar systems.
Experience with data orchestration, transformation, and pipeline tools such as Airflow, dbt, Fivetran, Stitch, Matillion, Informatica, Talend, Kafka, Spark, Dagster, Prefect, or similar platforms.
Strong understanding of data modeling, dimensional modeling, data warehousing, data lakes, data quality, data governance, APIs, event data, and secure data access.
Experience supporting business intelligence tools such as Tableau, Power BI, Looker, Mode, Sigma, Qlik, ThoughtSpot, or similar reporting platforms preferred.
Experience with cloud platforms such as AWS, Azure, or Google Cloud, including storage, compute, data processing, security, monitoring, and deployment services.
Ability to work with Data Analytics, Business Intelligence, Product, Engineering, Machine Learning, Finance, Customer Success, Revenue Operations, Security, IT, and business stakeholders.
Strong analytical thinking, troubleshooting ability, documentation skills, communication, collaboration, code review discipline, and follow-through.
Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Mathematics, Statistics, Analytics, or a related technical field preferred.
Advanced technical education, cloud certification, data engineering certification, analytics engineering training, or equivalent practical experience preferred.
Competitive compensation package.
Comprehensive medical, dental, and vision healthcare coverage.
Flexible remote-first work environment.
Performance bonus eligibility.
Data platform performance and analytics enablement incentive opportunities.
Long-term incentive opportunities where applicable.
Retirement savings plan with company contribution.
Professional development and continuing education reimbursement.
Data engineering, cloud platforms, AI-enabled analytics, machine learning enablement, data governance, analytics engineering, and technical leadership development resources.
Wellness and mental health support programs.
Paid time off and company holidays.
Opportunity to work on enterprise-level data platform modernization, AI-enabled data workflows, cloud infrastructure, analytics enablement, and business intelligence transformation initiatives.
Access to modern cloud data platforms, orchestration tools, transformation frameworks, BI platforms, collaboration systems, monitoring tools, and AI-enabled data engineering resources.
Strong data engineer with the ability to design reliable pipelines, build scalable datasets, and improve the quality of data used across the business.
Analytical and detail-oriented, with the ability to identify data inconsistencies, validate results, troubleshoot pipeline issues, and explain root causes clearly.
Business-minded and practical, with the ability to understand how data quality, reporting logic, and platform reliability affect leadership decisions and operational performance.
Technically disciplined, with strong ownership of code quality, documentation, testing, pipeline reliability, performance, and data security.
Collaborative and able to work effectively with analytics teams, product teams, engineers, finance partners, customer success teams, security teams, and business leaders.
Strong communicator who can explain data issues, technical tradeoffs, metric definitions, system dependencies, and project timelines to technical and non-technical audiences.
Process improvement-oriented, with the ability to identify manual work, reduce recurring data issues, automate workflows, and improve platform maturity.
Security-aware and responsible, with the ability to support secure data access, privacy expectations, data governance, and compliance requirements.
Curious and learning-oriented, with interest in modern data tools, AI-enabled analytics, cloud architecture, automation, and scalable engineering practices.
High integrity and discretion when handling customer data, financial information, product data, business performance metrics, employee information, and confidential company records.
Support data leadership with reliable pipeline development, data platform scalability, data quality improvement, and analytics-ready datasets.
Help improve business decision-making by ensuring leaders and teams have access to accurate, timely, and trusted data.
Support AI and machine learning initiatives by building clean, well-structured, secure, and accessible datasets for experimentation, model development, and production use.
Align data engineering work with business priorities, product goals, reporting needs, customer insights, revenue operations, finance planning, and enterprise growth objectives.
Strengthen data governance by improving documentation, data definitions, lineage visibility, access controls, ownership clarity, and quality monitoring.
Support business intelligence teams by delivering consistent metric logic, scalable data models, well-documented datasets, and reliable reporting foundations.
Contribute to enterprise performance improvement by enabling better visibility into customer behavior, product usage, financial trends, operational workflows, and revenue performance.
Partner with Product and Engineering teams to improve event tracking, product telemetry, data architecture, system integrations, and customer-facing analytics.
Partner with Security and IT teams to support privacy, access management, audit readiness, data protection, and secure platform operations.
Help turn data engineering into a strategic capability that supports innovation, operational efficiency, customer intelligence, and long-term enterprise value.
Remote-first data engineering role.
Periodic travel may be required for data strategy sessions, engineering offsites, analytics workshops, product planning meetings, company gatherings, or strategic technology reviews.
High-visibility technical role supporting enterprise data systems, analytics platforms, executive reporting, AI initiatives, and business intelligence.
Fast-paced environment focused on data reliability, platform scalability, AI-enabled workflows, reporting accuracy, security, and operational excellence.
Regular collaboration with Data Analytics, Business Intelligence, Product, Engineering, Machine Learning, Finance, Customer Success, Revenue Operations, Security, IT, and leadership teams.
Opportunity to influence data architecture, pipeline reliability, analytics quality, data governance, AI readiness, and enterprise reporting maturity.
Requires flexibility during pipeline incidents, reporting deadlines, data quality issues, system migrations, schema changes, product launches, audit requests, and urgent business analysis needs.
Role requires handling confidential customer data, financial information, product usage data, employee information, system credentials, business performance metrics, and internal documentation with discretion.
Data Engineering.
Cloud Data Platforms.
ETL and ELT Development.
Analytics Engineering.
Data Pipeline Development.
Data Warehouse Development.
Data Modeling.
Business Intelligence Enablement.
Data Quality Management.
Data Integration.
Machine Learning Data Support.
Data Governance Support.
SQL Development.
Cloud Data Infrastructure.
Remote Data Engineering.
Compensation Package: $188,000 – $270,000
Base Salary: $188,000 – $270,000.
Annual Performance Bonus.
Data Platform Performance Incentives.
Analytics Enablement and Data Quality Incentives.
Long-Term Incentive Eligibility where applicable.
Additional benefits may include:
• Comprehensive healthcare coverage.
• Retirement savings plan with company contribution.
• Wellness and mental health programs.
• Professional education assistance.
• Flexible remote work environment.
• Paid time off and company holidays.
• Data engineering, cloud platforms, Snowflake, Databricks, dbt, Airflow, analytics engineering, data governance, and AI-enabled data systems training.
• Access to modern cloud data platforms, orchestration tools, transformation frameworks, BI platforms, collaboration systems, monitoring tools, and AI-enabled engineering resources.
We are committed to fostering a workplace where innovation, diversity, and inclusion drive meaningful business outcomes. We provide equal employment opportunities to all applicants and employees regardless of race, religion, gender, age, disability, veteran status, sexual orientation, or any other protected characteristic under applicable law.
Build the data foundation that supports enterprise reporting, product analytics, customer intelligence, finance visibility, and AI-enabled business capabilities.
Work with modern cloud data platforms, orchestration tools, transformation frameworks, BI systems, and scalable engineering practices.
Partner with analytics, product, engineering, finance, and business teams to turn complex data into trusted information that improves decisions.
Contribute to a remote-first organization investing heavily in AI-enabled technology, cloud data infrastructure, automation, analytics, and digital transformation.
Improve data quality, pipeline reliability, reporting accuracy, and platform scalability through meaningful technical work.
Build a strong data engineering career platform by turning technical depth, data discipline, and business understanding into measurable enterprise value.
At Admiral Talent, our vision is to redefine staffing through genuine partnerships, innovation, and a people-first mindset. Guided by experienced leadership, we’re committed to driving growth, empowering talent, and delivering exceptional results for every client we serve.