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Hire dedicated remote data engineers who design, build, and optimize the data infrastructure your business depends on. From ETL pipelines and data warehousing to real-time streaming and analytics platforms, our pre-vetted data engineers integrate seamlessly into your team to deliver reliable, scalable data solutions that turn raw data into actionable intelligence.
Hire Data EngineersOur data engineers don't just write SQL and schedule cron jobs. They architect end-to-end data platforms using modern best practices, ensuring your data infrastructure is reliable today and scalable for tomorrow. Every engagement starts with understanding your data needs and designing a solution that fits.
See Our MethodologyWe design data systems starting with the pipeline layer. Reliable, idempotent, and observable pipelines form the backbone of every data solution. If the pipeline is solid, everything downstream works.
Our data engineers build on modern cloud platforms, leveraging managed services for scalability and cost efficiency. No over-provisioned servers, no maintenance overhead, just elastic infrastructure that grows with your data.
Quality is engineered into every layer, not bolted on after the fact. Schema validation, anomaly detection, freshness checks, and automated testing ensure your data is accurate, complete, and trustworthy at all times.
Without reliable data pipelines and well-architected infrastructure, every team in your organization suffers. Marketing can't measure ROI, product can't track usage, and leadership can't trust the numbers in their dashboards.
report that poor data quality undermines their analytics and decision-making capabilities. The root cause is almost always inadequate data engineering, not a lack of data.
Critical business data trapped in disconnected systems, spreadsheets, and legacy databases. Teams make decisions on incomplete information because no one has a unified view.
ETL jobs fail silently, data arrives late or corrupted, and downstream reports show conflicting numbers. Teams lose trust in data and revert to gut decisions.
Business insights are days or weeks old by the time they reach decision-makers. Competitors using real-time data move faster and capture opportunities you miss.
Data volumes double every year but your infrastructure doesn't scale. Queries slow to a crawl, pipelines time out, and storage costs spiral without a clear architecture strategy.
Comprehensive data engineering capabilities covering every layer of the modern data stack.
Design and build robust data pipelines that extract data from any source, transform it to meet business requirements, and load it into your target systems. Batch, micro-batch, and real-time patterns tailored to your needs.
Architect and implement modern cloud data warehouses on Snowflake, BigQuery, or Redshift. Dimensional modeling, slowly changing dimensions, and query optimization for fast, reliable analytics.
Implement event-driven architectures using Kafka, Kinesis, or Pub/Sub. Stream processing with Flink or Spark Streaming for real-time dashboards, alerts, and operational intelligence.
Build scalable data lakes on AWS S3, Azure Data Lake, or Google Cloud Storage. Implement lakehouse architectures with Delta Lake or Apache Iceberg for combined batch and streaming workloads.
Build the data models and semantic layers that power your BI tools. dbt transformations, materialized views, and optimized schemas that make dashboards fast and self-service analytics possible.
Implement data catalogs, lineage tracking, access controls, and automated quality checks. Ensure compliance with GDPR, HIPAA, and industry-specific data regulations across your entire data platform.
Deep expertise across the modern data stack from cloud platforms to visualization tools.
AWS
Google Cloud
Azure
Snowflake
Airbyte
Fivetran
dbt
Databricks
PostgreSQL
MongoDB
Redis
DynamoDB
BigQuery
Redshift
Looker
Power BIA structured engagement model that gets data engineers contributing to your team quickly and effectively.
Assess your current data landscape, sources, quality, and business requirements. Map existing pipelines, identify gaps, and define success metrics for data engineering outcomes.
Select pre-vetted data engineers matched to your technology stack, industry, and project complexity. You interview and approve every engineer before they join your team.
Design the target data architecture including pipeline patterns, storage layers, transformation logic, and data models. Create a detailed implementation roadmap aligned with business priorities.
Build and deploy data pipelines, warehouse schemas, and transformation layers. Iterative development with regular demos ensuring alignment with requirements at every stage.
Comprehensive testing of data accuracy, pipeline reliability, and query performance. Load testing at production scale, data quality validation, and performance tuning.
Deploy monitoring dashboards, alerting, and automated recovery. Data engineers continue as embedded team members providing ongoing development, optimization, and support.
Measurable improvements in data infrastructure performance and business intelligence capabilities.
Optimized pipelines
Automated monitoring
Cloud optimization
From days to hours
Our data engineers build infrastructure designed to handle 10x your current data volume without re-architecture. Cloud-native designs scale elastically with demand.
No more weekend firefighting when data volumes spike. Your pipelines handle growth gracefully.
Every pipeline is built with idempotency, error handling, retry logic, and comprehensive monitoring. When something breaks, it self-heals or alerts immediately.
Trust your data again. Reliable pipelines mean reliable dashboards, reports, and decisions.
Move from batch processing measured in days to near real-time analytics. Our engineers implement streaming architectures and optimized query patterns that deliver insights when they matter.
Business teams get answers in minutes, not days, enabling faster and more confident decision-making.
Hire senior data engineers at a fraction of local market rates without compromising on quality. Our pre-vetted engineers bring deep expertise in modern data platforms and best practices.
Save 40-60% compared to hiring locally while getting engineers who are productive from week one.
The modern data stack is vast and evolving rapidly. From orchestrating Airflow DAGs to optimizing Snowflake queries, from designing Kafka streaming topologies to building dbt transformation layers, data engineering demands deep technical expertise across dozens of tools and frameworks. A generalist developer simply cannot deliver the same pipeline reliability, query performance, and architectural soundness as a dedicated data engineer.
At 3Li Global, our data engineers come pre-vetted with hands-on experience across cloud platforms like AWS, GCP, and Azure, warehousing solutions like Snowflake and BigQuery, and modern ETL tools like dbt, Airbyte, and Databricks. They understand data modeling, slowly changing dimensions, schema evolution, and the operational realities of keeping petabyte-scale systems running reliably. Whether you need to build from scratch or optimize existing infrastructure, our engineers deliver.
Build your data infrastructure with expert engineers
Common questions about hiring remote data engineers and building data infrastructure.
Our data engineers have deep expertise across the major cloud data platforms including AWS (Redshift, Glue, S3, Kinesis), Google Cloud (BigQuery, Dataflow, Pub/Sub), Azure (Synapse, Data Factory, Data Lake), and Snowflake. They also work extensively with Databricks, dbt, Airflow, Kafka, and the broader modern data stack. We match engineers to your specific technology requirements.
Absolutely. Our data engineers build real-time streaming pipelines using Apache Kafka, AWS Kinesis, Google Pub/Sub, and Apache Flink. They implement event-driven architectures that process data in milliseconds for use cases like real-time dashboards, fraud detection, recommendation engines, and operational monitoring.
Data quality is engineered into every layer of the pipeline. Our engineers implement schema validation at ingestion, automated testing with tools like Great Expectations, freshness monitoring, anomaly detection, and data contracts between producers and consumers. Quality issues are caught and alerted on before they reach downstream consumers.
Data engineers build and maintain the infrastructure that makes data available and reliable, including pipelines, warehouses, data lakes, and transformation layers. Data scientists analyze that data to build models, discover insights, and create predictions. Think of data engineers as building the roads and data scientists as driving on them. Both are essential, but data engineers must come first.
Our data engineers typically begin contributing within the first week. During weeks one and two, they complete onboarding, understand your data landscape, and start on initial pipeline tasks. By week three or four, they are independently building and deploying pipelines. The structured onboarding process ensures rapid ramp-up without sacrificing quality.
Yes. Our data engineers implement comprehensive data governance including data catalogs, lineage tracking, access control policies, PII handling, encryption at rest and in transit, and audit logging. They ensure compliance with GDPR, HIPAA, SOC 2, and industry-specific regulations as part of every data platform they build.
Our data engineers have deep expertise across the major cloud data platforms including AWS (Redshift, Glue, S3, Kinesis), Google Cloud (BigQuery, Dataflow, Pub/Sub), Azure (Synapse, Data Factory, Data Lake), and Snowflake. They also work extensively with Databricks, dbt, Airflow, Kafka, and the broader modern data stack. We match engineers to your specific technology requirements.
Absolutely. Our data engineers build real-time streaming pipelines using Apache Kafka, AWS Kinesis, Google Pub/Sub, and Apache Flink. They implement event-driven architectures that process data in milliseconds for use cases like real-time dashboards, fraud detection, recommendation engines, and operational monitoring.
Data quality is engineered into every layer of the pipeline. Our engineers implement schema validation at ingestion, automated testing with tools like Great Expectations, freshness monitoring, anomaly detection, and data contracts between producers and consumers. Quality issues are caught and alerted on before they reach downstream consumers.
Data engineers build and maintain the infrastructure that makes data available and reliable, including pipelines, warehouses, data lakes, and transformation layers. Data scientists analyze that data to build models, discover insights, and create predictions. Think of data engineers as building the roads and data scientists as driving on them. Both are essential, but data engineers must come first.
Our data engineers typically begin contributing within the first week. During weeks one and two, they complete onboarding, understand your data landscape, and start on initial pipeline tasks. By week three or four, they are independently building and deploying pipelines. The structured onboarding process ensures rapid ramp-up without sacrificing quality.
Yes. Our data engineers implement comprehensive data governance including data catalogs, lineage tracking, access control policies, PII handling, encryption at rest and in transit, and audit logging. They ensure compliance with GDPR, HIPAA, SOC 2, and industry-specific regulations as part of every data platform they build.
Book a free consultation with our data engineering team. We'll assess your current data landscape, identify pipeline and infrastructure gaps, and show you how dedicated remote data engineers can accelerate your analytics capabilities.
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