Data Engineer and Data Analyst Leasing

Last updated: November 11, 2026

Reliable reporting and analytics depend on reliable data pipelines. 360 Soft provides data engineers and data analysts from Istanbul who build and run ETL/ELT pipelines, data warehouses and lakehouses, and the dashboards your teams use to make decisions. Specialists work remotely, on-site or in a hybrid model. You can add one engineer to your data team or build a complete data function; teams range from 1 to 200+ people and a typical start is within 1–2 weeks. This page is part of our IT staff leasing services.

Who needs a data engineer?

Companies whose reports are built manually in spreadsheets from several systems.
Organisations moving from an on-premises data warehouse to a cloud platform such as Snowflake, BigQuery, Databricks or Microsoft Fabric.
Product teams that need event data, customer data or operational data consolidated for analytics.
Businesses preparing data for machine learning or AI use cases.
Analytics teams whose data scientists spend more time cleaning data than analysing it.

Roles in a data team

Role	Focus
Data engineer	Pipelines, ingestion, transformation, orchestration, data warehouse and lakehouse modelling
Analytics engineer	Business-ready data models, dbt, data tests and documentation
Data analyst / BI specialist	Dashboards, KPI definitions, ad-hoc analysis, stakeholder reporting
Data platform / DataOps engineer	Infrastructure, CI/CD for data, monitoring, cost control
Data quality / governance specialist	Data quality rules, catalogue, lineage, access policies

Seniority matrix

Level	Profile	Typical responsibility
Junior (analysts)	Solid SQL and BI tool skills	Reports and dashboards from existing models
Mid	Independent delivery of pipelines or models	New sources, transformations, tests, documentation
Senior	Architecture and performance experience	Platform design, data modelling standards, mentoring
Lead	Data strategy and team leadership	Roadmap, governance, stakeholder alignment

Technology and skill matrix

Area	Technologies
Languages	SQL, Python, Scala
Processing	Apache Spark, Databricks, Kafka, Flink
Orchestration and transformation	Airflow, dbt, Azure Data Factory, AWS Glue, SSIS
Warehouses and lakehouses	Snowflake, BigQuery, Redshift, Synapse, Microsoft Fabric, Delta Lake
Databases	PostgreSQL, SQL Server, Oracle, MongoDB
BI	Power BI, Tableau, Looker, Qlik, Metabase

Technical validation: how we assess data engineer candidates

Candidates complete a SQL exercise, a data modelling discussion (for example designing a star schema for sales data) and a practical pipeline task, such as loading and deduplicating data from an API into a warehouse with tests. For analysts we review a dashboard exercise and the way they explain results. You interview the shortlisted candidates and decide.

Lease or hire in-house?

Leasing suits platform migrations, backlog peaks and skills you need for a defined period. In-house hiring suits a long-term data lead or a core team you can retain. Many companies combine both: an internal data owner with leased engineers and analysts who deliver the pipelines and reports.

Working models

Remote: the data engineer works from Türkiye through your VPN, VDI or bastion host, with MFA and logged access.
On-site: the data engineer works at your premises when physical presence is needed.
Hybrid: on-site during onboarding or critical phases, remote in steady state.
Shifts: business hours in your time zone, extended hours or 7/24 rotation.

Process and typical timelines

Step	What happens	Typical duration
1. Requirement call	Scope, systems, tools, hours, language, location	1–2 days
2. Shortlist	Screened profiles matched to the agreed criteria	A few working days
3. Assessment	Interview and a SQL and pipeline exercise	Within the first week
4. Onboarding	Accounts, access, documentation, first-week plan	Typical start in 1–2 weeks
5. Steady state	Monthly reporting and service reviews	Ongoing

KPI and SLA examples for data engineering

Indicator	Definition	How it is used
Pipeline success rate	Scheduled runs completed without failure	Daily monitoring, monthly report
Data freshness	Delay between source update and availability in the warehouse	Defined per dataset
Data quality test coverage	Critical tables covered by automated tests	Quarterly target
Incident resolution time	Time to fix failed pipelines, per priority	Defined in the SLA
Delivery	Planned sources, models or dashboards delivered per sprint	Sprint review
Platform cost	Compute and storage cost trend	Monthly cost review

Targets are agreed per engagement after a review of your data landscape.

Pricing factors

Factor	Effect
Role and seniority	Senior engineers and architects cost more than analysts
Technology	Specific platform expertise (for example Databricks or Kafka) narrows availability
Scope	Real-time streaming and complex governance add effort
Hours and support	On-call for production pipelines increases cost
Team size and duration	Larger, longer engagements are more efficient
Model	Managed data operations include monitoring and SLA ownership

Data Engineer and Data Analyst Leasing — overview

In-house, freelancer and outsourcing compared

Criterion	In-house hire	Freelancer	360 Soft data engineer
Time to start	Often months	Fast but variable	Typically 1–2 weeks
Continuity	Depends on retention	Single point of failure	Replacement process in the contract
Coverage	Limited by headcount	Limited to one person	Shifts up to 7/24, teams up to 200+
Admin load	Recruitment, payroll, HR	Contract per person	One B2B contract
Security and IP	Your policies	Varies	NDA, IP assignment, GDPR DPA

Risks and how we mitigate them

Risk	Mitigation
Undocumented pipelines	Code in version control, data catalogue, documentation standards
Poor data quality	Automated tests, monitoring and alerting
Access to sensitive data	Masking, role-based access, separate environments
Knowledge loss on exit	Replacement process and documented handover

Security, GDPR/KVKK, NDA and intellectual property

The data engineer signs confidentiality undertakings before access is granted and works only inside your controls: your identity provider, your logging, your approval workflows. Access follows least privilege and is revoked on offboarding. Personal data is processed only on your documented instructions.

Legal note

The engagement is a B2B service agreement with 360 Soft, which employs the specialist under Turkish labour law. The standard package includes an NDA, an assignment of intellectual property in work results to the client, and a GDPR data processing agreement (DPA) with the Standard Contractual Clauses for EU transfers or the UK IDTA/Addendum for UK transfers. Processing in Türkiye is also subject to the KVKK (Law No. 6698). Sector rules in Türkiye apply where the team operates, and local rules in your country apply as well; we recommend a review by your legal counsel.

Selection checklist

Are the platforms, sources and target models defined?
Has the candidate passed SQL and pipeline exercises?
Are code review, version control and testing practices agreed?
Is access to personal data minimised and documented?
Is there a written replacement process?
Are NDA, IP and GDPR DPA in place?

Illustrative scenario

Illustrative scenario. A retail company builds its weekly reports by hand from its ERP, e-commerce platform and CRM. It leases two data engineers to build pipelines into a cloud warehouse and one BI specialist to rebuild the core dashboards on governed data models. After launch, one engineer stays on to run and extend the platform. This example is illustrative and does not describe a specific client.

Frequently asked questions

What does a leased data engineer do?

They build and operate data pipelines, model data in warehouses or lakehouses, implement data quality tests and keep data available for reporting and analytics.

What is the difference between a data engineer and a data analyst?

A data engineer builds the data infrastructure and models; a data analyst uses that data to create reports, dashboards and analyses.

Which platforms do you cover?

Snowflake, BigQuery, Databricks, Redshift, Synapse, Microsoft Fabric, Airflow, dbt, Spark, Kafka, Power BI, Tableau and others.

Can you support a migration to a cloud data platform?

Yes. Engineers can plan and execute migrations from on-premises warehouses to cloud platforms.

How do you test candidates?

With SQL exercises, a data modelling discussion and a practical pipeline task, followed by your own interview.

Can data engineers work remotely?

Yes. Most data work is done remotely through secure access; on-site or hybrid work is possible.

How quickly can someone start?

A typical start is within 1–2 weeks after the profile is agreed.

What drives the price?

Role, seniority, technology stack, scope, support hours, team size and service model.

How do you protect personal data?

With a GDPR data processing agreement, SCCs for EU transfers, data minimisation, masking and KVKK compliance.

Can you provide a complete data team?

Yes. We can combine data engineers, analytics engineers, BI specialists and a lead into one team.

Talk to us about your team

Tell us the role, the number of people, the working model (remote, on-site or hybrid) and your target start date. We reply with a team proposal, a scope and an indicative timeline. Call or WhatsApp +90 850 335 10 87, message us on WhatsApp, email info@360-soft.com or use our contact page.

360 Soft Marketing Yazılım ve Danışmanlık Ltd. Şti. · Nidakule Ataşehir Batı, Barbaros Mah. Begonya Sk. No:1 D:2, 34746 Ataşehir/İstanbul, Türkiye.

Related services

All services: IT Staff Leasing