Highlight
Modern data stack expertise
dbt, Snowflake/BigQuery, and orchestration with CI/CD discipline to keep analytics fresh.
Hire data engineers who make analytics trustworthy.
Analytics engineers and platform leads who keep pipelines reliable and governed.
Deeptal data engineers design models, pipelines, and observability so analytics stay accurate and compliant.
Hire a top data engineer nowNo-risk trial. Pay only if satisfied.
Clients rate Deeptal data teams 4.9 / 5.0 on average.
Pulse surveys after onboarding and milestone readouts.Compensation snapshot
Bench-readyAnnual bands across key markets to plan budgets confidently.
US & Canada
$125k – $175k
Glassdoor Oct 2025, total comp
United Kingdom
£60k – £85k
Glassdoor Oct 2025, total comp
Germany
€60k – €85k
Glassdoor Oct 2025, total comp
The Balkans
€35k – €65k
Glassdoor Oct 2025, total comp
Avg. seniority
8.3 yrs
Launch readiness
10-14 days
From brief to onboarding
Data quality
SLAs + lineage
Installed in sprint 1
Trusted by product and engineering teams
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Delivery highlights
What you get with Deeptal
Senior talent, clear rituals, and proactive communication from week one.
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Quality and governance first
Tests, lineage, PII handling, and documentation so teams trust the numbers.
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Operational visibility
Monitoring, alerting, and incident playbooks for pipelines, ensuring stakeholders know when data is safe to use.
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Business-aligned modeling
Domain modeling that maps to KPIs and downstream consumers, reducing rework and confusion.
Coverage map
Where this team drives outcomes
Where data engineers move the needle
Common engagements we run for data leaders, FP&A, and product analytics.
- Modern data platform builds with ingestion, modeling, and BI enablement.
- Data reliability programs: tests, lineage, alerting, and SLAs.
- Streaming and real-time analytics with Kafka/Kinesis and warehouse sinks.
- Governance and privacy implementations for regulated industries.
How we cover the stack
Engineers with depth across ingestion, transformation, and activation.
- Ingestion: Fivetran/Airbyte/custom connectors and CDC pipelines.
- Transformation: dbt, SQL modeling, semantic layers, and documentation.
- Orchestration: Airflow, Dagster, Prefect with CI/CD and testing.
- Activation: reverse ETL, metrics layers, and analytics enablement.
Specialties
Specialist coverage by pod
Modeling & warehousing
Dimensional + semantic layersData contractsCost + performance tuningDocumentation + training
Pipelines & orchestration
Airflow/Dagster/PrefectCDC + streamingTesting + CI/CDDeployment automation
Governance & quality
Lineage + observabilityPII handling + accessSLAs for data productsIncident playbooks
Enablement
BI partnershipMetrics layersStakeholder commsHandover + runbooks
Sample talent
Meet ready-to-start specialists
Profiles curated for your stack, time zones, and delivery rituals.
Helena V.
Data Platform Lead
Berlin | CET
Snowflake, dbt, Airflow, Terraform
Built governed data platform for ecommerce scale, introduced data contracts, lineage, and alerting that cut incident noise by 40%.
Rajesh M.
Senior Analytics Engineer
Chicago | CST
BigQuery, dbt, Looker, GitLab CI
Modeled revenue and product funnels with semantic layers and CI-tested dbt models; partnered with FP&A on KPI definitions.
Lena P.
Streaming Data Engineer
Cluj-Napoca | CET
Kafka, Flink, Python, Snowflake
Delivered near-real-time logistics dashboards with streaming ingestion, privacy-safe transformations, and cost-aware retention policies.
Hiring playbook
How to hire data engineers
Data engineers should balance platform discipline with business empathy.
Align on business questions
- List the decisions and KPIs that matter most. This shapes modeling and testing expectations.
- Ask candidates how they defined metrics and prevented divergence across teams.
Probe data quality and governance
- Discuss how they implemented tests, lineage, PII handling, and access controls.
- Look for a track record of reducing incidents and clarifying ownership.
Validate modeling and performance instincts
- Review their approach to semantic layers, query optimization, and cost management.
- Great engineers can explain trade-offs between granularity, freshness, and spend.
Check orchestration and DevOps habits
- Explore their CI/CD setup for pipelines, code review practices, and rollback strategies.
- Ask how they monitor pipeline health and communicate incidents to stakeholders.
Onboard with clarity
- Share data sources, compliance constraints, and BI consumers up front.
- Pair them with analytics and platform leads in sprint one to align on contracts and rituals.
How it works
Engage in three clear steps
1
Talk to a delivery lead
Share your data goals, sources, and governance needs. We anchor screening to the outcomes you need, not just tool lists.
2
Meet hand-selected talent
Within days you see a short list of data engineers calibrated to your stack, rituals, and time zones.
Average time to match is under 24 hours once the brief is clear.
3
Start with a no-risk sprint
Kick off with a trial sprint and clear success criteria. Swap or scale the team quickly if the fit is not perfect.
Pay only if satisfied after the initial milestone.
Exceptional talent
How we source the top data engineers
We continuously screen analytics and platform specialists so teams mobilize fast without sacrificing quality.
Every engineer is assessed for depth, collaboration, and delivery habits—not just tool familiarity.
Thousands apply each month. Only top talent are accepted.
Step 1
Language & collaboration evaluation
Communication, collaboration signals, and product intuition checks to ensure they can lead as well as build.
Step 2
In-depth skill review
Technical assessments and architecture conversations tailored to ingestion, modeling, governance, and reliability scenarios.
Step 3
Live screening
Optional: Your team can join
Live exercises to test problem solving, observability instincts, and quality bar under real-time constraints.
Step 4
Test project
Optional: You can provide your own brief
A short-term project to validate delivery habits, communication cadence, and production readiness in your domain.
Step 5
Continued excellence
Ongoing scorecards, engagement reviews, and playbook contributions to stay on the Deeptal bench.
Capabilities
Capabilities of data engineers
Our data teams excel in modeling, quality, governance, and activation—shipping trustworthy analytics fast.
Ingestion and CDC
Batch and streaming pipelines with change-data capture, retries, and monitoring.
Transformation and modeling
dbt and SQL modeling with semantic layers, tests, and documentation aligned to business domains.
Orchestration and CI/CD
Airflow/Dagster/Prefect with version control, automated tests, and safe deploys.
Quality and observability
Data tests, anomaly detection, lineage, and alerting tied to SLAs and owners.
Governance and privacy
Access control, PII handling, and compliance-minded processes with clear audit trails.
Performance and cost
Warehouse optimization, storage tiering, and cost dashboards to keep spend predictable.
Activation and BI enablement
Reverse ETL, metrics layers, and enablement rituals with analytics and business teams.
Streaming and real-time
Event pipelines, stream processing, and low-latency dashboards for operational decision making.
Trusted by data and finance leaders
Find the right data talent for every project
From analytics engineers to platform leads, Deeptal teams match your stack, rituals, and governance needs.
Analytics engineers
Modelers focused on semantic layers, tests, and BI enablement.
Data platform engineers
Specialists in ingestion, orchestration, security, and observability.
Streaming specialists
Engineers who build real-time pipelines and dashboards with reliability in mind.
Data reliability leads
Leaders who implement contracts, SLAs, and incident playbooks to keep data trustworthy.
FAQs
How much does it cost to hire a data engineer?▾
Costs vary by region, seniority, and data domain. Glassdoor data from October 2025 shows median total compensation for data engineers around $138,000 in the US, £78,000 in the UK, and €80,000 in Germany. We calibrate teams to your governance needs and budget before kickoff.
How quickly can I meet vetted PostgreSQL talent?▾
Most clients see calibrated shortlists within 48 hours and can start a trial within 10–14 days once the brief is clear.
How do you vet data quality and governance skills?▾
We review portfolios, run data-focused screens, and use test projects to confirm testing, lineage, PII handling, and governance experience. References validate production impact.
Can I hire hourly, part-time, or full-time?▾
Yes. We place data engineers on hourly, part-time, or full-time engagements depending on your backlog and budget.
What if the first match is not right?▾
We replace quickly at no additional cost during the trial and continue until you are confident in the match.
Explore services
Explore related Deeptal services
Looking for end-to-end delivery? Browse Deeptal programs across technology, marketing, and consulting.
Hiring guide
How to hire data engineers
Hiring data engineers means balancing platform rigor with business outcomes.
Use this guide to vet for reliability, governance, and partnership with stakeholders.
Is demand for data engineers high?
Yes. As companies lean on analytics for decisions, demand for reliable data pipelines continues to rise.
Engineers who combine governance with delivery speed are the hardest to find.
What distinguishes great data engineers?
A focus on quality, lineage, and ownership of data products.
Ability to design semantic layers that reflect how the business measures itself.
Operational discipline: CI/CD, monitoring, incident response, and communication.
Core layers to cover
Ingestion and storage: connectors, CDC, lake/warehouse setup, and retention.
Transformation and modeling: dbt, tests, documentation, and metrics layers.
Activation and governance: BI enablement, reverse ETL, access control, and compliance.
When to choose specialists vs. generalists
Choose specialists for streaming, heavy governance, or complex migration work.
Choose generalists for analytics enablement and product-focused data needs.
How to run the process
Define the consumers, KPIs, and compliance constraints.
Review past models and pipelines, then use live discussions on quality and reliability.
Pilot with a small data product or quality sprint to validate collaboration.
Median total compensation (Glassdoor, Oct 2025, USD equivalent)
USA
$138,000
Canada
$105,000
United Kingdom
$78,000
Germany
$80,000
Romania
$48,000
Ukraine
$52,000
India
$19,000
Australia
$115,000
Top data engineers are in high demand.
Move fast with analytics talent, transparent reporting, and a trial sprint to prove the fit.