Dedicated data engineers and analysts trained in modern data stack tools who build ETL pipelines, data warehouses, BI dashboards, and ML infrastructure. Full-time, timezone-aligned, starting at $1,499/mo.
A remote data engineer is a specialist who designs, builds, and maintains the data infrastructure that powers business intelligence and machine learning — including ETL/ELT pipelines, data warehouses, streaming systems, and analytics platforms. They work full-time from a remote location, embedded in your engineering team, ensuring your data is reliable, accessible, and ready for decision-making.
Every data engineer in our pool is vetted across these core tools and platforms. No generalists learning on the job.
Concrete deliverables, not vague "data support." These are the outputs your engineer ships from week one.
Build and maintain robust data pipelines using Airflow, dbt, and Spark that extract data from diverse sources, transform it into analytics-ready formats, and load it into your warehouse on schedule. Your engineer implements idempotent pipelines with proper error handling, retry logic, and alerting so data flows reliably without manual intervention. They version-control all transformations and maintain documentation that keeps your team aligned.
Design star and snowflake schemas in Snowflake, BigQuery, or Redshift that optimize for both query performance and storage efficiency. Your engineer models dimensions and fact tables aligned to your business domains, implements slowly changing dimensions for historical tracking, and builds materialized views that keep dashboard queries fast as your data scales from gigabytes to terabytes.
Create interactive dashboards in Looker, Tableau, or Power BI that surface actionable insights for stakeholders across your organization. Your engineer designs semantic layers and data models that enable self-service analytics, builds automated reports for executive review, and optimizes query performance so dashboards load in seconds even against large datasets.
Implement data validation frameworks, automated quality checks, and lineage tracking that catch issues before they reach dashboards or ML models. Your engineer sets up Great Expectations or dbt tests across critical pipelines, monitors freshness and completeness metrics, and builds data catalogs that make your warehouse navigable and trustworthy for every team that depends on it.
Build feature stores, model training pipelines, and inference endpoints that bridge the gap between data science experiments and production ML systems. Your engineer sets up MLflow or Databricks for experiment tracking, automates model retraining on fresh data, and deploys serving infrastructure that handles prediction requests at scale with monitoring for data drift and model degradation.
Implement streaming architectures with Kafka and Flink for real-time analytics, event processing, and operational dashboards. Your engineer designs event schemas, builds consumer applications that process millions of events per day, and creates real-time aggregations that power live dashboards, fraud detection, inventory tracking, and any use case where batch processing is too slow.
Real results our data engineers deliver. Not hypothetical projections — actual outcomes from client engagements.
A B2B SaaS company had data scattered across 12 different sources — Stripe, Salesforce, Intercom, product database, Google Analytics, and seven other tools. A Syentrix data engineer built a unified data warehouse in Snowflake with dbt transformations and Airflow orchestration. Consolidated all 12 sources into a single source of truth. Reporting that previously took three days of manual spreadsheet work now runs automatically in 15 minutes, enabling the leadership team to make data-driven decisions in real time.
An eCommerce retailer with 50,000 SKUs across multiple warehouses needed real-time inventory visibility and dynamic pricing. A Syentrix data engineer built a Kafka-based streaming pipeline processing 2 million events per day from POS systems, warehouse scanners, and supplier feeds. Real-time inventory accuracy improved by 40 percent, stockouts dropped by 60 percent, and the dynamic pricing engine increased gross margins by 8 percent within the first quarter.
A fintech lender needed to automate credit scoring at scale while maintaining regulatory compliance. A Syentrix data engineer built an end-to-end ML pipeline on Databricks that ingests applicant data, engineers features from 30 data sources, trains gradient boosting models, and serves predictions via a low-latency API. The pipeline processes 100,000 applications per day with 99.9 percent uptime, reducing manual review by 75 percent and improving default prediction accuracy by 22 percent.
Fixed monthly rates. No hourly markups. No recruiter fees. Full-time, dedicated data talent embedded in your engineering team.
Full-time, dedicated data engineer
2-3 data specialists for end-to-end coverage
All plans include onboarding, tool integration, dedicated client success manager, and 30-day replacement guarantee.
See full pricing details →Your data lives in dozens of tools — CRM, billing, product database, marketing platforms — and nobody trusts the numbers because every team has a different spreadsheet. You need a data engineer who consolidates everything into a single source of truth with automated pipelines and reliable dashboards.
You have data scientists building models in notebooks, but no infrastructure to deploy them to production. You need a data engineer who bridges the gap between experimentation and production ML — building feature stores, training pipelines, and serving infrastructure that scales.
You have outgrown Excel and Google Sheets for business reporting. You need a data engineer who can design your first warehouse, build automated pipelines from your existing tools, and create dashboards that replace manual reporting workflows your team has been doing for years.
If your data needs are fully served by spreadsheets and you just need someone to build formulas and pivot tables, a full-time data engineer is overkill. You would be better served by a virtual assistant with spreadsheet skills or a one-time consulting engagement.
If you need a single database migration or a one-time data cleanup, hire a contractor for the project. Our data engineers are full-time team members who build and maintain ongoing data infrastructure. They are designed for continuous work, not isolated tasks.
Everything you need to know about hiring a remote data engineer through Syentrix.
A Syentrix data engineer designs, builds, and maintains the data infrastructure that powers your analytics and machine learning initiatives. They build ETL/ELT pipelines using tools like Airflow, dbt, and Spark, architect data warehouses in Snowflake, BigQuery, or Redshift, and create BI dashboards in Looker, Tableau, or Power BI. Unlike contractors who deliver a pipeline and leave, our data engineers work full-time embedded in your team, monitoring data quality, optimizing query performance, and evolving your data architecture as your business scales.
Our data engineers are proficient across the modern data stack. For orchestration and ETL: Apache Airflow, dbt, Apache Spark, AWS Glue, and Azure Data Factory. For warehousing: Snowflake, Google BigQuery, and Amazon Redshift. For visualization: Looker, Tableau, and Power BI. For streaming: Apache Kafka and Apache Flink. For ML infrastructure: Databricks, TensorFlow, and MLflow. They also work with Python, SQL, and cloud platforms including AWS, GCP, and Azure. We match engineers based on your specific stack requirements.
Yes. Our data engineers are matched to your specific technology stack. Whether you run Snowflake on AWS, BigQuery on GCP, or a hybrid multi-cloud setup, we have engineers with hands-on production experience in your exact environment. During the matching process, we review your current architecture, tools, and data sources to ensure your engineer can be productive from day one. They integrate with your existing CI/CD pipelines, version control workflows, and data governance frameworks without requiring you to change how your team operates.
Our vetting process has four stages designed to filter for engineers who build production-grade data systems. Stage one reviews their portfolio of data architectures with verified scale metrics. Stage two is a live pipeline design exercise where candidates architect a solution for a real-world data problem including schema design, orchestration, and error handling. Stage three tests depth across SQL optimization, distributed systems, data modeling, and cloud infrastructure. Stage four is a two-week paid trial on supervised projects. Our acceptance rate for data engineers is 4.8 percent.
A data engineer builds and maintains the infrastructure that makes data usable — pipelines, warehouses, data models, and integrations. They focus on reliability, scalability, and data quality at the systems level. A data analyst uses that infrastructure to extract insights, build dashboards, run queries, and support business decision-making. Think of data engineers as building the roads and data analysts as driving on them. Many teams need both. Our individual plan covers a data engineer, while our pod plan can include a data engineer plus a data analyst plus an ML engineer for complete coverage.
Data engineering is one piece. Build the complete engineering team.
Tell us about your data infrastructure goals. We will match you with a pre-vetted data engineer within 48 hours — no commitment, no cost for the consultation.