We are a dynamic and rapidly expanding enterprise technology organization operating in a data-intensive environment where scalable data platforms, trusted pipelines, accurate analytics, AI readiness, automation, and secure information flow directly influence long-term business performance. Our organization supports enterprise customers, product teams, analytics groups, executive leadership, machine learning initiatives, finance operations, customer success teams, revenue operations, and business functions that depend on reliable data to make confident decisions.
As modern companies grow, data engineering becomes one of the most critical foundations for product innovation, customer intelligence, operational visibility, financial planning, machine learning, and enterprise strategy. Organizations need senior data engineers who can go beyond routine ETL development. They need professionals who can design scalable data architecture, improve pipeline reliability, strengthen data quality, support cloud data modernization, enable analytics teams, and build data systems that can support both current business needs and future AI-driven capabilities.
Our company is investing heavily in cloud data platforms, data warehouse modernization, data lake architecture, AI-enabled analytics, real-time data workflows, data governance, pipeline automation, data quality monitoring, analytics engineering, machine learning feature pipelines, business intelligence enablement, and enterprise reporting transformation. We are seeking a highly capable Senior Data Engineer who can help build the data foundation that supports intelligent decisions, scalable operations, and long-term business growth.
This is not a traditional data engineering role focused only on moving data between systems or maintaining basic database jobs. The Senior Data Engineer will serve as a senior technical contributor responsible for designing reliable data pipelines, building scalable data models, improving data infrastructure, optimizing cloud data platforms, supporting analytics and AI use cases, and helping the organization turn complex data ecosystems into trusted business assets.
The selected candidate will work closely with Data Analytics, Business Intelligence, Data Science, Machine Learning Engineering, Product, Software Engineering, Finance, Revenue Operations, Customer Success, Security, IT, and Executive Leadership to ensure data systems are accurate, secure, scalable, well-documented, and aligned with business priorities.
This is a strong opportunity for a senior data engineering professional who wants to work with modern cloud platforms, influence enterprise data architecture, support AI-enabled transformation, and help build a more mature, trusted, and scalable data ecosystem within a remote-first organization.
As Senior Data Engineer, you will design, build, optimize, and maintain enterprise data pipelines, data models, transformation workflows, data integrations, data warehouse structures, and cloud-based data systems.
You will be responsible for improving the reliability, performance, quality, and scalability of data platforms used by analytics, data science, product, finance, customer success, revenue, and leadership teams.
You will serve as a senior technical partner across data and business teams while maintaining accountability for data quality, pipeline reliability, platform performance, documentation standards, security expectations, and long-term data architecture.
The ideal candidate combines deep SQL expertise, cloud data platform experience, strong engineering discipline, data modeling skill, business understanding, and the ability to mentor others while delivering high-quality technical work.
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.
Lead development of reliable data models that support business intelligence, executive reporting, product analytics, customer analytics, revenue operations, finance reporting, and operational decision-making.
Integrate data from enterprise systems such as CRM, ERP, product platforms, customer success tools, finance systems, marketing systems, operational tools, support platforms, APIs, and third-party data sources.
Partner with Data Analytics and Business Intelligence teams to define data requirements, improve metric logic, strengthen reporting foundations, and deliver trusted datasets.
Partner with Data Science and Machine Learning teams to support feature engineering, model training datasets, experimentation data, AI workflows, predictive analytics, and data product development.
Partner with Product and Engineering teams to improve event tracking, product telemetry, customer behavior data, platform data architecture, and customer-facing analytics capabilities.
Develop and maintain transformation logic using SQL, Python, dbt, Spark, Airflow, Dagster, Prefect, Kafka, or similar tools depending on platform and business needs.
Support and optimize cloud data platforms such as Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, or similar enterprise data environments.
Improve data quality through validation rules, reconciliation checks, anomaly detection, monitoring, automated testing, lineage documentation, and issue resolution processes.
Optimize pipeline performance, warehouse cost, query speed, storage efficiency, compute usage, data freshness, error handling, and operational reliability.
Create and maintain technical documentation, data dictionaries, lineage notes, source-to-target mappings, transformation logic, data contracts, runbooks, and operational procedures.
Partner with Security, Compliance, and IT teams to support role-based access controls, privacy requirements, secure data handling, audit readiness, data retention, and governance expectations.
Troubleshoot complex pipeline failures, data discrepancies, schema changes, broken reports, slow queries, system integration issues, and production data incidents.
Mentor data engineers and analytics engineers by providing code review support, design feedback, documentation guidance, and practical technical coaching.
7+ years of experience in data engineering, analytics engineering, database development, ETL development, cloud data platforms, data infrastructure, or business intelligence engineering.
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.
Advanced SQL skills with experience writing complex queries, joins, window functions, stored procedures, query optimization, 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, orchestration, or pipeline development.
Strong 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, lakehouse architecture, 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, deployment services, and cost optimization.
Experience designing production-grade data workflows with strong testing, monitoring, documentation, alerting, version control, and operational support practices.
Ability to work with Data Analytics, Business Intelligence, Data Science, Machine Learning Engineering, Product, Software Engineering, Finance, Revenue Operations, Customer Success, Security, IT, and business stakeholders.
Strong analytical thinking, troubleshooting ability, technical 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 engineering 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, platform architecture, 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, governance tools, and AI-enabled data engineering resources.
Senior data engineer with the ability to design reliable pipelines, build scalable datasets, and improve the quality of data used across the enterprise.
Strong systems thinker with the ability to understand source systems, downstream reporting needs, data dependencies, platform constraints, and long-term architecture requirements.
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, platform reliability, and analytics readiness affect leadership decisions.
Technically disciplined, with strong ownership of code quality, documentation, testing, pipeline monitoring, platform performance, and data security.
Collaborative and able to work effectively with analytics teams, data scientists, product teams, engineers, finance partners, revenue 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 data platform maturity.
Security-aware and responsible, with the ability to support secure data access, privacy expectations, data governance, audit readiness, and compliance requirements.
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 scalable pipeline development, data platform architecture, data quality improvement, and analytics-ready data assets.
Help improve business decision-making by ensuring leaders and teams have access to accurate, timely, consistent, and trusted data.
Support AI and machine learning initiatives by building clean, well-structured, secure, and accessible datasets for experimentation, model development, feature engineering, and production workflows.
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, metric 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 contracts, system integrations, and customer-facing analytics.
Partner with Security and IT teams to support privacy, access management, audit readiness, data protection, secure platform operations, and data retention requirements.
Help turn data engineering into a strategic capability that supports innovation, operational efficiency, customer intelligence, AI readiness, and long-term enterprise value.
Remote-first senior 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, Data Science, Machine Learning Engineering, Product, Software Engineering, Finance, Revenue Operations, Customer Success, 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.
Senior Data Engineering.
Cloud Data Platforms.
ETL and ELT Development.
Analytics Engineering.
Data Pipeline Architecture.
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: $270,000 – $360,000
Base Salary: $270,000 – $360,000.
Annual Performance Bonus.
Data Platform Performance Incentives.
Analytics Enablement and Data Quality Incentives.
AI Readiness and Data Infrastructure 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, machine learning data pipelines, and AI-enabled data systems training.
• Access to modern cloud data platforms, orchestration tools, transformation frameworks, BI platforms, collaboration systems, monitoring tools, governance systems, 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, machine learning, and AI-enabled business capabilities.
Work with modern cloud data platforms, orchestration tools, transformation frameworks, BI systems, and scalable engineering practices.
Partner with analytics, data science, product, engineering, finance, revenue, 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, governance, and digital transformation.
Improve data quality, pipeline reliability, reporting accuracy, and platform scalability through meaningful senior-level technical work.
Build a strong senior 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.