Dedicated data engineer
Works full time on your product as part of your team on a rolling monthly basis.
Best for ongoing product development.Hire by specialist
Hire Data Engineers to Build Reliable Pipelines and Warehouses
Engineers who make data trustworthy and available: pipelines that run on time, models people understand and a platform analysts can rely on.
- Pipelines
- Warehousing
- Data quality
- Orchestration
- Interview first Meet the people before you commit
- Flexible terms Dedicated, part-time or project
- Works in your tools Your repos, boards and chat
- You own the code IP stays with you
Why hire a Data Engineer from Cruzata
Analytics and AI both depend on dependable data. Without engineering discipline, pipelines break silently, definitions drift and teams argue about whose number is right.
Our data engineers build ingestion, transformation and serving layers with testing, documentation and monitoring, so downstream dashboards and models can be trusted.
- Engagement
- Dedicated, part-time or project
- Selection
- Interview before you commit
- Tools
- Your repos, boards and chat
- Reporting
- Regular progress updates
Data Engineer skills and responsibilities
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Pipelines
Batch and streaming ETL / ELT.
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Warehousing
Dimensional models and lakehouse design.
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Data quality
Tests, contracts and anomaly alerts.
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Orchestration
Airflow, dbt and scheduling.
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Governance
Access control, lineage and cataloguing.
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Performance
Partitioning, indexing and cost tuning.
What our data engineers work on
A single source of truth.
- Source integration
- Warehouse build
- Semantic layer
- BI enablement
Fresh data for operations.
- Event streaming
- CDC replication
- Live dashboards
- Alerting
Feed models reliably.
- Feature pipelines
- Training datasets
- Vector stores
- Evaluation data
Engagement options for a data engineer
Part-time / hourly
A set number of hours each week or month for focused help.
Best for smaller scopes or reviews.Project-based
Fixed scope and timeline for a defined outcome.
Best when requirements are clear.How to hire a data engineer in five steps
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1
Share requirements
Stack, seniority, duration and goals.
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2
Review profiles
We propose suitable engineers.
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3
Interview
Speak to the people before you decide.
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4
Onboard
Access, tooling and ways of working.
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5
Deliver
Work starts with regular reviews.
Technology stack
Pipelines
- Python
- SQL
- Apache Airflow
- dbt
- Airbyte / Fivetran
- Spark
Platforms
- Snowflake
- BigQuery
- Redshift
- Databricks
- PostgreSQL
- S3 / ADLS
Streaming & BI
- Kafka
- Kinesis / Pub/Sub
- Debezium
- Power BI
- Tableau
- Looker
Where this fits
Solutions
- AI SolutionsAI & Automation
- System IntegrationTechnology
- Digital TransformationTechnology
- Business AutomationAI & Automation
Services
Industries
Data Engineer: frequently asked questions
A data engineer builds the infrastructure and pipelines that collect, clean, transform and store data for analytics and machine learning.
Data engineers build the platforms and pipelines; data analysts use the data to answer questions and build reports.
It depends on scale, cloud, skills and cost. We recommend after reviewing your needs.
Yes. We build the data layer to work with Power BI, Tableau, Looker and others.