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Data pipelines
Reliable ingestion, transformation, orchestration, retries, backfills, and operational visibility.
Staff Augmentation · Data Engineers
Hire Data Engineer
Data engineers who build reliable pipelines, trustworthy warehouse models, real-time systems, and governance your analytics and AI teams can depend on.
- 72 hours
- to a vetted shortlist
- 14-day trial
- replace at no extra fee
- Your tools
- Repos, tickets, standups
- 0+ Years building data platforms
- 0+ Data engineers available
- 0+ Pipelines in production
- 0% Target pipeline reliability
Trusted by ambitious teams
Built with teams that expect more.
A selection of organisations that have trusted Techora to design, build, and scale critical digital products.
What you can hire
Data engineers matched to your platform and consumers
Batch, streaming, warehouse, lakehouse, or operational data. We match the engineer to volume, latency, governance, and downstream use.
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Cloud warehouses
Snowflake, BigQuery, Redshift, and dimensional models that remain understandable.
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Lakehouse platforms
Databricks, Delta Lake, Spark, and governed layers for analytics and machine learning.
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Real-time data
Kafka, event streams, CDC, and low-latency processing for operational decisions.
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Analytics engineering
dbt models, tests, lineage, documentation, and semantic definitions shared across teams.
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Data governance
Ownership, access, quality, retention, lineage, and controls designed into the platform.
Why teams hire us
Data people trust enough to use
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Reliable pipelines
Retries, idempotency, backfills, alerting, and ownership prevent silent data loss.
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Metrics mean one thing
Shared definitions and modeled business logic reduce dashboard-by-dashboard disagreement.
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Freshness is visible
Consumers know when data arrived, whether it is complete, and who owns the issue.
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Costs stay controlled
Partitioning, clustering, workload design, and observability tie spend to useful work.
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Governance supports delivery
Access and lineage help teams move safely instead of creating a ticket maze.
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AI gets a stronger foundation
Versioned, tested, discoverable data improves training, retrieval, and production features.
Technologies
What our data engineers use every week
Warehouses
Snowflake, BigQuery, Redshift, PostgreSQL
Lakehouse
Databricks, Spark, Delta Lake, Iceberg
Pipelines
Airflow, Dagster, dbt, Kafka, Fivetran
Quality and governance
Great Expectations, DataHub, OpenLineage, Monte Carlo
How hiring works
Three steps. Then the data platform has an owner.
We match for data sources, scale, latency, consumers, governance, and the maturity of your current platform.
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Data brief
We map sources, consumers, SLAs, platform, quality pain, and the first delivery milestone.
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Technical interview
Review a pipeline design, data model, backfill strategy, or incident together.
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Start
The engineer joins platform access and the first pipeline milestone within a week.
Engagement
Flexible engagement. The same senior bar.
Data architecture
Part-time senior support for platform design, modeling, governance, and difficult migrations.
Dedicated data engineer
A full-time engineer embedded with analytics, platform, product, or AI teams.
Data pod
Data engineering, analytics engineering, cloud, and quality coverage for a platform stream.
Once they join
How they work inside your team
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Map sources and consumers
Systems, owners, SLAs, transformations, and business use become one visible flow.
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Define contracts
Schemas, freshness, quality, ownership, and change expectations are explicit.
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Build for recovery
Idempotency, checkpoints, replay, backfills, and reconciliation are designed from day one.
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Model the business
Warehouse layers separate source complexity from stable business concepts.
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Observe the platform
Freshness, volume, quality, lineage, and cost are visible before users report a problem.
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Enable self-service
Documentation, discovery, and trusted models reduce dependence on the data team.
Start this week
Tell us the sources, platform, and data outcome.
Share your stack and current bottleneck. We return interview-ready data engineering profiles within two business days.
- Snowflake & Databricks
- dbt & Airflow
- Batch & Streaming
- Data Quality
- Cloud Warehouses
- Senior Data Engineers