dbt modellers, Airflow pipeline engineers, Spark developers, Kafka streaming specialists, and Snowflake/BigQuery architects -- screened live on your data stack. Shortlisted in 72 hours. Flat 15% fee.
Trusted by 150+ data teams globally
250+
Vetted Data Engineers
72h
To Shortlist
15%
Flat Fee
Top 5%
Acceptance Rate
250+
Vetted Data Engineers
95%
Pipeline Delivery Rate
$0
Matching Fee
72h
To Shortlist
15%
Flat Management Fee
14-day
Free Replacement
Data engineers matched on your exact data warehouse, pipeline orchestrator, and transformation layer
A bad data engineer does not just write slow queries. They build pipelines that produce wrong numbers, break silently, and become impossible to maintain. Here is how we prevent that.
Every engineer passes a live technical screen: advanced SQL (window functions, CTEs, optimisation), data modelling (Kimball vs Data Vault vs One Big Table), pipeline design trade-offs, and a tool-specific challenge for your stack (dbt model design, Airflow DAG architecture, or PySpark job optimisation). We do not accept self-reported experience.
A dbt modeller on Snowflake is not the same as a Spark engineer on Databricks, which is not the same as a streaming engineer on Kafka and Flink. We match on your exact warehouse (Snowflake, BigQuery, Redshift, Databricks), orchestrator (Airflow, Prefect, Dagster), and transformation approach (dbt, Spark, custom Python). Stack specificity is mandatory.
We cover both sides: data engineers who build and maintain pipelines (ingestion, orchestration, infrastructure) and analytics engineers who model data for business consumption (dbt models, Kimball dimensional modelling, BI layer design). Tell us which problem you are solving and we match the right profile.
Pipelines that run without data quality guarantees are worse than no pipelines -- they produce wrong numbers quietly. We match engineers with data quality experience: Great Expectations, dbt tests, Soda, Monte Carlo, or custom alerting. Data observability is a screening criterion, not an afterthought.
Snowflake and Databricks consulting partners routinely charge $250-400/hr for engineers who cost $80-100/hr. Our fee is a flat 15% on the engineer rate, published in every contract. You see exactly what the engineer earns and pay nothing to receive your shortlist.
Data pipelines that break at 3am with no documentation or runbooks are a team problem. Every engagement includes weekly delivery reports, pipeline documentation standards, incident escalation paths, and a dedicated account manager. You get a managed data team, not an unaccountable freelancer.
Anonymised profiles from our active pool. Every engineer below has passed a live SQL screen, data modelling assessment, and tool-specific pipeline design challenge.
Analytics Engineer -- dbt & Snowflake
South Asia · 7 years
Built dbt project with 400+ models for a SaaS company's Snowflake warehouse. Reduced dashboard query time from 90s to 4s via materialisation strategy. Implemented dbt testing reducing data quality incidents by 80%.
Spark / Databricks Platform Engineer
Eastern Europe · 8 years
Built lakehouse platform on Databricks for a fintech processing 2TB/day. Designed medallion architecture (Bronze/Silver/Gold) with Delta Live Tables. Reduced pipeline compute costs 40% via adaptive query execution tuning.
Streaming Data Engineer -- Kafka & Flink
Eastern Europe · 6 years
Designed event streaming platform on Confluent Kafka processing 50M events/day for a retail analytics platform. Built real-time fraud detection pipeline with Flink, reducing fraud loss 35%. P99 latency under 200ms.
Data Platform Engineer -- GCP & BigQuery
Latin America · 5 years
Built GCP data platform for a 10M-user e-commerce company: Pub/Sub ingestion, Dataflow transformation, BigQuery storage with column-level security, and dbt-modelled reporting layer consumed by 50+ Looker dashboards.
Data Engineer -- Airflow & Redshift
South Asia · 5 years
Migrated 200+ Cron-based ETL jobs to Airflow on MWAA for an enterprise retailer. Built Fivetran + dbt ELT architecture replacing custom ETL code, reducing pipeline maintenance time by 70%.
Data Quality & Observability Engineer
Western Europe · 9 years
Implemented end-to-end data observability platform for a healthcare data company: Monte Carlo for anomaly detection, custom dbt tests for business logic validation, and SLA alerting reducing data incidents reported by stakeholders by 90%.
All profiles are anonymised. Full profiles including pipeline samples and references are shared after brief submission. Rates shown are engineer rates before the 15% management fee.
Stack-matched shortlisting in 72 hours. Every engineer pre-screened on SQL, data modelling, and your specific pipeline tools.
Tell us your exact stack: data warehouse (Snowflake, BigQuery, Redshift, Databricks), orchestration tool (Airflow, Prefect, Dagster), transformation approach (dbt, Spark, custom Python), and whether you need a data engineer or analytics engineer. The more specific, the faster we shortlist.
Our team reviews your brief and matches engineers from our vetted pool who have production experience on your specific tools. A dbt modeller on Snowflake is not interchangeable with a Spark engineer on Databricks. Stack specificity is mandatory in our matching process.
Every engineer on your shortlist has passed a live technical screen: advanced SQL (window functions, CTEs, query optimisation), data modelling design (Kimball vs Data Vault vs One Big Table), pipeline trade-off questions, and a tool-specific challenge (dbt model design, Airflow DAG architecture, or PySpark job tuning). No portfolio claims accepted.
Once you select an engineer, we manage the engagement: weekly delivery reports, pipeline documentation standards (README, data dictionaries, runbooks), incident escalation paths, and a dedicated account manager. You get a managed data team with accountability -- not an unmonitored freelancer. Delivery also includes dbt sources.yml documentation with freshness assertions configured for all upstream data sources, a dbt test coverage report confirming at least one data quality test per model (not_null, unique, accepted_values, or custom), and alert threshold configuration for pipeline failures and SLA breaches in the client's orchestration tool.
From dbt warehouse modelling to real-time Kafka streaming and Databricks lakehouse design. Specify your stack in the brief.
dbt model design on Snowflake, BigQuery, Redshift, and Databricks SQL. Kimball dimensional modelling, Data Vault 2.0, and One Big Table patterns. Incremental model strategy, materialisation decisions, dbt macro authoring, and semantic layer implementation.
Event streaming architecture on Confluent Kafka, AWS MSK, and Google Pub/Sub. Apache Flink and Spark Structured Streaming for stateful stream processing. CDC (Change Data Capture) with Debezium for real-time data replication from operational databases.
ELT pipeline engineering with Fivetran, Airbyte, and dlt for managed and custom ingestion. Orchestration design on Airflow (MWAA, Cloud Composer, Astronomer), Prefect, and Dagster. DAG architecture, dependency management, and SLA monitoring.
Databricks lakehouse design: Unity Catalog, Delta Live Tables, medallion architecture (Bronze/Silver/Gold). PySpark performance tuning, adaptive query execution, shuffle optimisation, and Spark cluster right-sizing. Apache Iceberg and Apache Hudi table format migrations.
End-to-end data observability platforms: Monte Carlo, Soda, and custom alerting. dbt test design (schema, data, custom macro tests), Great Expectations suites, freshness monitoring, volume anomaly detection, and SLA-based alerting for stakeholder-visible data incidents.
End-to-end cloud data platform design: ingestion, storage, transformation, and BI layer. Multi-cloud data architectures, data mesh implementations, and data platform cost optimisation (query cost governance, compute right-sizing, storage tiering). Data governance and column-level security.
Hiring the wrong role wastes months. Here is the exact distinction and the trigger for each hire.
Pipeline infrastructure
Builds and operates the systems that move and store data. Owns ingestion, orchestration, storage architecture, and infrastructure reliability.
Owns
Does NOT own
Business logic in SQL models, BI layer design
Hiring trigger
Data is not arriving reliably or pipelines are breaking
Data modelling for business
Transforms raw data into clean, documented, business-ready models using dbt. Bridges the gap between raw pipeline output and analyst/BI consumption.
Owns
Does NOT own
Pipeline infrastructure, ingestion tooling
Hiring trigger
Data is arriving but analysts cannot trust or use it
Statistical modelling & ML
Builds predictive models, runs experiments, and extracts statistical insights. Consumes clean data produced by data engineers and analytics engineers.
Owns
Does NOT own
Pipeline infrastructure, transformation modelling
Hiring trigger
You have clean data and need prediction or experimentation
Every layer of the data stack, the dominant tools in each category, and the tier of adoption.
dbt Core / Cloud
SQL-first modelling, testing, docs
Apache Spark / PySpark
Large-scale distributed transforms
Apache Beam
Batch + streaming unified model
Pandas / Polars
Small-medium Python transforms
SQLMesh
dbt alternative with native versioning
Apache Airflow (MWAA, Astronomer)
Dominant DAG orchestrator
Prefect
Python-native, dynamic workflows
Dagster
Asset-based orchestration
dbt Cloud (jobs)
For dbt-only orchestration
Temporal
Durable workflow engine
Snowflake
SaaS cloud DW, separation of compute/storage
BigQuery
GCP serverless DW, columnar storage
Databricks
Lakehouse platform, Delta Lake + Spark
Redshift
AWS columnar DW, Serverless option
DuckDB / MotherDuck
Embedded OLAP, fast local analytics
Apache Kafka (Confluent, MSK)
Event streaming backbone
Apache Flink
Stateful stream processing
Spark Structured Streaming
Micro-batch streaming on Databricks
Google Pub/Sub
GCP managed messaging
AWS Kinesis
AWS managed streaming
Fivetran
Managed connectors, automatic schema migration
Airbyte
Open-source connectors, self-hosted option
dlt (data load tool)
Python-native, schema-inferred loading
Stitch
Simple ELT, Singer taps
Debezium
CDC from operational databases
dbt tests (generic + custom)
Schema & data logic validation
Great Expectations
Expectation suites in Python
Monte Carlo
ML-based anomaly detection, lineage
Soda
SodaCL YAML-defined checks
Elementari
dbt-native observability layer
8 competency domains. Every engineer in our pool has been assessed live across SQL, modelling, pipeline design, and their specialist tool area.
Skill levels reflect our minimum screening threshold for pool inclusion. Not every engineer scores at the maximum -- specialisation depth varies by tool and role type.
Questions that reveal real pipeline depth. Generic coding problems tell you nothing about data modelling judgement or production pipeline thinking.
Walk me through how you would design a dbt project for a SaaS company moving from raw Salesforce and Stripe data to a Snowflake mart layer consumed by Looker.
Why this question
Tests end-to-end analytics engineering thinking: source staging conventions, intermediate joins, mart design (accounts/opportunities/revenue), dbt model materialisation choices, and Looker LookML awareness. Strong candidates distinguish staging vs intermediate vs mart layers and explain why.
Red flag answer
I would build one model that joins everything together. Weak candidates produce a single wide table.
Your Airflow DAG fails at 3am on a Wednesday, halfway through a 6-hour pipeline run that loads 200GB to Snowflake. Walk me through your incident response and how you prevent it from causing wrong data in dashboards.
Why this question
Tests pipeline resilience thinking: idempotency, partial load detection, downstream dependency pause, stakeholder communication, and runbook quality. Strong candidates have thought about the 'dashboard wrong at 9am' scenario before.
Red flag answer
I would re-run the DAG. No mention of idempotency or partial data in the target table.
A PySpark job that processes 500GB of user events daily takes 4 hours on a Databricks cluster. You need it under 45 minutes. What is your optimisation approach?
Why this question
Tests distributed computing depth: data skew detection, shuffle joins vs broadcast joins, partitioning strategy, AQE settings, cluster sizing (driver vs executor), caching, and file format (Delta vs Parquet). Weak candidates only suggest 'add more nodes'.
Red flag answer
I would increase the cluster size. No mention of skew, shuffle, or partitioning.
Design a streaming pipeline to detect fraudulent transactions in real time. You have 10M events per day from a payment processor via Kafka. SLA is 500ms from event to alert. What is your architecture?
Why this question
Tests streaming architecture: Kafka consumer group design, Flink stateful processing (keyed streams), windowing strategy, state backend (RocksDB), event time vs processing time, late arrival handling, and alert output sink. Strong candidates discuss at-least-once vs exactly-once processing.
Red flag answer
I would read from Kafka and write to a database. No mention of stateful processing or windowing.
How do you design a data quality strategy for a warehouse with 50 dbt models, 3 ingestion sources, and 20 dashboard consumers who escalate data incidents to the data team?
Why this question
Tests data quality maturity: dbt test coverage (schema + data + custom macro tests), freshness monitoring, volume anomaly detection, stakeholder-visible SLA alerting vs noisy internal alerts, and incident escalation runbooks. Strong candidates distinguish between 'tests that catch issues' and 'tests that prevent stakeholder complaints'.
Red flag answer
I would add dbt tests on the primary key. No mention of freshness, volume, or alert routing.
Your company's Snowflake bill jumped from $8,000 to $22,000/month over 3 months. How do you investigate and reduce it without breaking existing pipelines?
Why this question
Tests FinOps thinking for data warehouses: QUERY_HISTORY analysis, warehouse auto-suspend/auto-resume configuration, materialisation strategy (table vs incremental vs view), clustering key review, query result cache usage, and Snowflake resource monitor setup. Strong candidates prioritise investigation before action.
Red flag answer
I would pause some warehouses. No systematic investigation approach.
Cannot explain dbt layer conventions
Uses dbt but cannot distinguish staging, intermediate, and mart layers or explain why the separation matters.
Treats pipelines as one-and-done scripts
No mention of idempotency, backfill handling, or what happens when a pipeline re-runs on the same data.
Optimises Spark by adding nodes first
Skips diagnosing data skew, shuffle, or partitioning. Adding nodes is a last resort, not a first response.
No data quality ownership
Thinks data quality is the analyst's problem. Data engineers who don't write tests produce pipelines that produce wrong numbers silently.
Cannot read a query execution plan
Cannot explain what EXPLAIN ANALYZE output means or how to identify a full table scan vs index/cluster use.
No documentation culture
Has never written a runbook, data dictionary, or dbt model description. Pipelines that aren't documented are liabilities when engineers leave.
| Region | Junior (2-4 yrs) | Mid (4-7 yrs) | Senior (7+ yrs) |
|---|---|---|---|
| South Asia | $40-65/hr | $65-90/hr | $90-120/hr |
| Eastern Europe | $55-75/hr | $75-110/hr | $110-140/hr |
| Latin America | $45-70/hr | $70-100/hr | $100-130/hr |
| South-East Asia | $35-55/hr | $55-85/hr | $85-115/hr |
| Western Europe | $80-110/hr | $110-145/hr | $145-180/hr |
| North America | $90-130/hr | $130-175/hr | $175-220/hr |
All rates are engineer rates before the OTF 15% management fee. Rates shown reflect dbt, Airflow, Spark, and major cloud warehouse experience. Kafka/Flink specialists typically command a 10-20% premium.
Published engineer rates before the OTF 15% management fee. Rates reflect dbt, Airflow, Spark, and major cloud warehouse experience.
Junior (2-4 yrs)
dbt + Airflow basics
Mid-level (4-7 yrs)
Production warehouse experience
Senior (7+ yrs)
Multi-stack, data platform design
Junior (2-4 yrs)
Strong SQL, Python ETL
Mid-level (4-7 yrs)
Spark/Kafka specialists common
Senior (7+ yrs)
Lakehouse architects, streaming experts
Junior (2-4 yrs)
GCP and BigQuery strong region
Mid-level (4-7 yrs)
dbt + cloud warehouse focus
Senior (7+ yrs)
Data platform architects
Junior (2-4 yrs)
Python, SQL, basic pipelines
Mid-level (4-7 yrs)
Airflow, Redshift, BigQuery
Senior (7+ yrs)
Data platform design
Junior (2-4 yrs)
Strong engineering background
Mid-level (4-7 yrs)
Enterprise data platform experience
Senior (7+ yrs)
Principal/staff data engineers
Junior (2-4 yrs)
Entry-level with CS degree
Mid-level (4-7 yrs)
FAANG-adjacent pipeline experience
Senior (7+ yrs)
Staff/principal with cross-stack depth
Kafka / Flink streaming specialists
+15-25%Databricks Unity Catalog experience
+10-20%Data Vault 2.0 modelling
+10-15%dbt + Monte Carlo observability stack
+10-20%HIPAA / GDPR data governance
+15-25%FinOps with warehouse cost reduction track record
+10-20%Premium percentages are applied to base regional rates. Engineers with multiple specialisations (e.g., dbt + Monte Carlo + GDPR) command the higher end of combined premiums.
Stack-specific vetting is the difference. A generic data engineer hire from a broad marketplace costs the same and builds pipelines that break silently.
| Criterion | Open IT Freelancers | Toptal | Upwork | Staffing Agency |
|---|---|---|---|---|
| Vetting method | Live SQL screen, data modelling assessment, tool-specific pipeline challenge | Algorithm & coding tests -- limited data modelling depth | Self-reported profile only | CV review and reference check |
| Stack specificity | Matched on warehouse + orchestrator + transformation approach | General 'data engineer' category | Tag-based search, no stack validation | General placement, not stack-specific |
| Data quality screening | Required -- dbt tests, observability tool experience assessed | Not systematically screened | Not screened | Not typically screened |
| Management fee | Flat 15% -- published in every contract | ~30-40% markup (undisclosed) | 10-20% platform fee on engineer | 25-40% markup, often undisclosed |
| Time to shortlist | 72 hours | 1-2 weeks | Immediate (unvetted pool) | 1-3 weeks |
| Matching fee | $0 | $0 (fee built into rates) | $0 (fee built into rates) | $0-5,000+ |
| Pipeline documentation standard | Required -- README, data dictionary, runbooks included | Engineer's discretion | Not managed | Varies, usually not enforced |
| Replacement guarantee | 14-day free replacement | Trial period (varies) | No guarantee | Varies (often 30-90 days, fee applies) |
| Weekly delivery reporting | Yes -- dedicated account manager | Not included | Not included | Sometimes, at extra cost |
| Data Engineer vs Analytics Engineer distinction | Yes -- separate pools and screening paths | Limited distinction | No distinction | Rarely distinguished |
| Kafka / Flink streaming specialists | Available in vetted pool | Limited availability | Unvetted availability | Hard to source reliably |
A mid-level data engineer at $90/hr working 40 hrs/week for 20 weeks = $72,000 engineer cost.
Open IT Freelancers
$82,800
You pay $82,800 total
Toptal (est. 35%)
$97,200
~$14,400 more than OTF
Agency (est. 40%)
$100,800
~$18,000 more than OTF
Rates are illustrative. OTF fee is published -- others are estimates based on typical market markups.
Different industries have different data engineering requirements. Tell us your industry and we match the right specialist.
SaaS companies need analytics engineering to make product metrics trustworthy. Typical hires: dbt modellers who can take raw Salesforce, Stripe, and product event data to clean mart layers consumed by Looker or Metabase. MRR, churn, and activation funnels need reliable definitions -- not ad-hoc SQL.
Fintech companies need real-time streaming pipelines for fraud detection and risk scoring, plus audit-grade data lineage and GDPR/PCI-DSS compliance. Engineers with Kafka and Flink experience, plus strict data governance requirements, are critical. Pipelines that produce wrong risk scores have direct revenue impact.
E-commerce teams need high-volume ELT pipelines (orders, inventory, customer behaviour) feeding BI tools for merchandising and operations. Black Friday scale testing, real-time inventory updates via CDC, and customer segmentation models are common requirements. GCP/BigQuery is prevalent in this segment.
Healthcare data engineering requires HIPAA-compliant pipelines with row-level security, PII masking, and full audit trails. HL7 FHIR data integration, clinical trial data pipelines, and EHR system integration are specialist areas. Data quality is a regulatory requirement, not an option.
These are the types of briefs we can shortlist for within 72 hours. The more specific, the faster the match.
dbt Core, Snowflake, Airflow (MWAA), Looker
Series B SaaS company. Raw data from Salesforce, HubSpot, Stripe, and product event stream in S3. Need dbt project designed from scratch: staging, intermediate, and mart layers. Target state: 60+ models, <2min dashboard query time, full dbt test coverage. 4-month engagement.
Kafka (Confluent), Apache Flink, BigQuery, Python
Fintech payments company processing 8M transactions/day. Need real-time fraud scoring pipeline: Kafka consumer, Flink stateful aggregations (rolling 1hr/24hr windows), feature store writes, and alert output to ops dashboard. P99 latency target 300ms. SOC 2 audit requirements.
PySpark, Databricks, Delta Lake, Unity Catalog, Airflow
Enterprise retailer migrating from legacy Hadoop cluster to Databricks lakehouse. 3TB/day data volume. Medallion architecture design (Bronze/Silver/Gold), Unity Catalog RBAC setup, Airflow DAG migration, and PySpark job performance tuning. Target: 40% compute cost reduction vs current Hadoop.
No fee to post. Shortlist in 72 hours. Flat 15% if you hire.
Everything about hiring vetted data engineers, analytics engineers, and streaming specialists through Open IT Freelancers.
Before You Hire
Written by our engineering team - no fluff, just the frameworks that work.
Developer rates by region, hidden cost breakdown, TCO comparison across DIY vs managed platforms, and a complete budgeting guide.
The 7-day vetting framework, technical interview questions, take-home test design, and red flags - built for non-technical founders.
Stage-by-stage decision framework for founders and CTOs. Cost, speed, and risk tradeoffs - with a clear verdict for each company type.
Stack-matched shortlist in 72 hours. No fee to post, no upfront cost. Flat 15% only when you hire.