Hire Remote Data Engineers

Hire Data Engineers Who Build Data Infrastructure That Analysts and Scientists Actually Trust

Bad data in, bad insights out. Most companies don’t have a data analysis problem — they have a data engineering problem. Inconsistent pipelines, undocumented transformations, tables that don’t refresh reliably, and a data warehouse that analytics engineers are afraid to change because nobody knows what downstream systems depend on what.

We match you with senior Data Engineers who’ve built reliable data infrastructure for high-growth SaaS companies, e-commerce platforms, and enterprise analytics organizations. Engineers who design data pipelines that don’t break silently, data models that make sense to everyone who uses them, and the data platform architecture that scales from millions to billions of rows without a ground-up rebuild.

Start in days, not months. Pay 50% less than equivalent US-based data engineering talent.

What Our Data Engineers Build

Data Pipeline Architecture (ELT/ETL)

Airbyte, Fivetran, and custom connector-based data ingestion from production databases, APIs, SaaS tools, and event streams. dbt transformation layers that are version-controlled, tested, and documented — data pipelines that analysts can trust and modify safely.

Data Warehouse Design

Snowflake, BigQuery, Redshift, and Databricks warehouse architecture: schema design (dimensional modeling, data vault), partitioning and clustering strategies, query optimization, and the access control model that gives each team the data they need without exposing what they shouldn’t see.

Real-Time & Streaming Data

Kafka, Kinesis, and Flink-based streaming data pipelines for use cases requiring real-time data: event tracking, real-time dashboards, fraud detection feeds, and CDC (Change Data Capture) from production databases. Designing the trade-off between streaming freshness and batch pipeline simplicity correctly.

Data Quality & Observability

Great Expectations, dbt tests, and Monte Carlo-based data quality frameworks — validating data at ingestion, transformation, and serving layers. Automated data quality alerts that catch pipeline failures before analysts discover stale or incorrect data in dashboards.

Data Platform & Self-Service Analytics

Building the data platform that lets analysts and data scientists work independently: well-documented dbt models, a semantic layer (dbt Semantic Layer, Looker LookML), and the data cataloging that makes discovering and trusting data assets possible without asking a data engineer.

Data Engineering Technology Stack

Orchestration: Airflow (managed: MWAA, Astronomer), Dagster, Prefect, dbt Cloud

Transformation: dbt (core and cloud), Spark SQL, Great Expectations

Ingestion: Airbyte, Fivetran, Stitch, custom Python connectors, Debezium (CDC)

Streaming: Apache Kafka, Kinesis, Flink, Spark Streaming, Pub/Sub

Warehouses: Snowflake, BigQuery, Redshift, Databricks, ClickHouse

Languages: Python, SQL, Scala (Spark), Go (custom tooling)

Client Success Story: Series B SaaS — Data Warehouse Built from Scratch, Analyst Velocity 5x

A Series B SaaS company with 3 analysts was running all analysis in Excel off Postgres exports — a manual, error-prone process that produced inconsistent results and couldn’t scale as the team grew. Our Data Engineer built a modern data stack over 10 weeks: Airbyte for data ingestion from their production Postgres database and 6 SaaS tools (Salesforce, HubSpot, Stripe, Intercom, Mixpanel, and Zendesk), Snowflake as the warehouse, dbt for transformation with 80+ tested models covering their core business entities, and Looker for self-service analytics. Analyst time spent on data extraction and preparation dropped 80%. The analytics team grew from 3 to 8 without adding data engineering headcount. New reporting questions went from “2-3 week turnaround” to “same-day self-service.”

Client Success Story: E-Commerce Platform — Real-Time Inventory Data Prevents $3M in Lost Sales

A high-volume e-commerce company was experiencing a costly problem: inventory counts in their analytics system were 4–6 hours behind their production database, causing the merchandising team to make purchasing decisions based on stale data. Popular items were repeatedly selling out before reorders were triggered. Our Data Engineer implemented a CDC (Change Data Capture) pipeline using Debezium and Kafka to stream inventory changes from their production PostgreSQL database to their Snowflake warehouse with sub-minute latency. Inventory data freshness went from 4–6 hours to under 60 seconds. Stockout events dropped 43%. The merchandising team estimated $3M in prevented lost sales in the first year based on stockout frequency reduction.

Why Companies Choose Our Data Engineers

• Reliability obsession: They build pipelines that fail loudly and recover predictably — not pipelines that silently produce wrong data
• Analytics-first data modeling: They design data models that analysts and scientists can actually use without asking a data engineer what every column means
• Modern stack fluency: dbt, Airbyte, Dagster, and Snowflake — they work in the tools that modern data teams run
• 50% cost savings: Senior data engineering expertise at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Individual Data Engineer — One senior data engineer owning your data pipeline, warehouse, and data platform architecture.
• Data Engineering + Analytics Pod — Data Engineer handling infrastructure paired with an Analytics Engineer or Data Analyst owning the dbt semantic layer and business logic.
• Data Platform Teams — Multiple data engineers for large organizations building centralized data platforms serving many internal analytics consumers.
• Contract-to-Hire — Evaluate a data engineer’s pipeline design quality and data modeling approach before committing long-term.

How To Vet Data Engineers

Our vetting identifies data engineers who build reliable, maintainable data infrastructure — not just functional pipelines that nobody else can maintain.

1. Data modeling exercise — Given a business context (SaaS subscription business, e-commerce, etc.), design a dimensional data model for a core analytics use case. We evaluate schema design decisions, grain definition, and slowly changing dimension handling.
2. dbt code review — Review dbt models they’ve written. We evaluate model structure, test coverage, documentation, and whether the code is maintainable by another engineer.
3. Pipeline reliability interview — How do they handle late-arriving data? What happens when a source API goes down? How do they handle schema changes in upstream systems? We assess operational maturity.
4. Data quality approach — What data quality checks do they implement? At what points in the pipeline? How do they alert on quality failures? What’s their approach when analysts find incorrect data?

What to Look for When Hiring Data Engineers

Strong data engineers treat data infrastructure with the same engineering rigor as application infrastructure.

What strong candidates demonstrate:

• Their dbt projects have tests on every model — not just transformation code with no validation
• They document data models and transformation logic so analysts can understand the data without asking
• They design for operational failures: backfill strategies, late-arriving data handling, and idempotent pipeline design
• They measure pipeline health with freshness SLOs and data quality metrics — not just “the pipeline is running”

Red flags to watch for:

• Pipelines with no tests, no documentation, and no alerting on failure — works until something breaks, then nobody knows why
• No data modeling knowledge — treats the warehouse as an operational database replica rather than an analytics-optimized layer
• No experience with dbt or modern transformation tooling — uses custom Python scripts without version control or testing
• Has never implemented data quality checks — relies on downstream analysts to discover data problems

Interview questions that reveal real depth:

• “Walk me through the data model you’d design for a SaaS subscription business. How do you handle plan changes, trial periods, and annual-to-monthly conversions in your fact and dimension tables?”
• “A pipeline has been producing incorrect revenue data for 3 days and nobody noticed until an analyst caught it. How do you diagnose the root cause and what process changes do you put in place?”
• “When would you choose a streaming data pipeline over a batch pipeline? Walk me through a specific decision you’ve made.”

Related Services

• Data Scientists — ML engineers who build prediction and recommendation models on the data infrastructure data engineers create.
• Data Analysts — Analytics engineers and BI developers who build the dashboards and reports that data engineers’ pipelines power.
• Infrastructure Engineers — Cloud infrastructure engineers who design the underlying compute and storage that data platforms run on.
• AI Product Engineers — Engineers who productize ML models and AI features that depend on robust data infrastructure.

Want to Hire Remote Data Engineers?

We source, vet, and place senior Data Engineers who build reliable data pipelines, well-modeled warehouses, and the data platform infrastructure that analytics teams and data scientists can actually depend on. Whether you need one data engineer to build your first modern data stack or a team to manage a large-scale data platform, we make it fast, affordable, and low-risk.

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Ready to hire Data Engineers who build data infrastructure your team can trust? Contact us today and we’ll introduce you to senior data engineers within 48 hours.