Data Engineers for AI-Ready Pipelines

Hire Data Engineers
Who Make Data Dependable

Hire Data Engineers who make product, analytics, finance, operations, and AI teams trust the same data. Build reliable ingestion, ELT, streaming, warehouse, lakehouse, dbt, orchestration, quality, lineage, and cost controls your team can operate after launch.

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Senior Data Engineer

Airflow dbt Spark Kafka

All Levels

$4,800/mo

Junior from $2,400/mo · Mid from $3,500/mo · Senior from $4,800/mo

7-Day Risk-Free Trial

Zero commitment start

Onboard in 48 Hours

Pre-vetted, ready to ship

AI-Native Development

Faster iteration, cleaner code

Trusted by CTOs, Engineering Leaders & Operators Worldwide

10+ Years in Business

500+ Projects Delivered

200+ Global Clients

4.9/5 Client Satisfaction

Why Companies Struggle to Hire Data Engineers

AI and analytics fail when pipelines are fragile, metric definitions drift, and nobody owns freshness, lineage, contracts, backfills, and quality. A Data Engineer hire should remove that uncertainty, not add another layer of scripts.

The Hiring Problem

Dashboards, AI features, and board reports disagree because source definitions, dbt models, and warehouse tables are not governed as one system

Pipelines fail silently, retry unsafely, duplicate records, or overwrite history without clear backfill and recovery rules

Events arrive late, schemas change, joins drift, and nobody can explain whether a metric is stale, incomplete, or simply wrong

Warehouse and streaming costs rise because partitioning, clustering, workload sizing, query shape, and retention are not owned

Our Solution

Engineers build dependable batch, ELT, CDC, and streaming pipelines with Airflow, Dagster, Prefect, dbt, Spark, Kafka, Kinesis, Pub/Sub, and cloud-native services

Warehouse and lakehouse models are designed for Snowflake, BigQuery, Redshift, Databricks, Postgres, S3, GCS, or Azure data platforms without locking the team into brittle assumptions

Data contracts, dbt tests, freshness checks, lineage, observability, alerting, backfill plans, and recovery workflows are added where they protect real decisions

Storage layout, partitioning, clustering, incremental models, compute usage, and query patterns are tuned so performance and cost move together

Why Hire Data Engineers from Devlyn

Senior, product-minded Data Engineers vetted for data modeling, reliability habits, cost discipline, security awareness, and the ability to make data usable for product, analytics, finance, operations, and AI teams.

Pipeline Engineering

Creates batch, ELT, CDC, and streaming pipelines for product events, billing, sales, finance, operations, support, IoT, and application data.

Warehouse Architecture

Models clean, query-ready datasets in Snowflake, BigQuery, Redshift, Databricks, Postgres, lakehouse tables, and object storage with clear grain and ownership.

Data Orchestration

Schedules, monitors, retries, backfills, and recovers workflows using Airflow DAGs, Dagster assets, Prefect flows, or cloud-native orchestration.

Transformation With dbt

Builds tested, documented dbt models with reusable metric logic, source freshness, semantic consistency, lineage, and owner-friendly documentation.

Data Quality Controls

Catches schema drift, missing records, late events, duplicates, invalid values, referential breaks, null spikes, and freshness gaps before users lose trust.

Cost and Performance Tuning

Improves query speed and reduces spend through partitioning, clustering, incremental models, materialization choices, workload tuning, caching, and retention policies.

How hiring actually works.

No procurement cycle, no mystery shortlists. Six steps from first call to first shipped feature, with timelines you can defend to leadership.

A 30-minute call to map the data problem, source systems, warehouse or lakehouse, orchestration tools, BI and AI consumers, freshness targets, security constraints, timezone overlap, and why the Data Engineer role is the right hire. If the real gap is analytics engineering, platform engineering, ML engineering, or a small pod, we say that before you interview anyone.

Within 24 hours, you receive pre-vetted Data Engineer profiles matched against your stack and outcome: Airflow, Dagster, Prefect, dbt, Spark, Kafka, cloud warehouses, data lakes, event streams, CDC, quality checks, lineage, and cost-aware processing. Each profile includes technical context, availability, communication fit, and why the engineer belongs in your interview loop.

Use the interview loop to test how the engineer handles data modeling, ETL and ELT design, event streams, idempotent loads, schema drift, late-arriving data, backfills, dbt tests, warehouse performance, lineage, and cost-aware processing. You can run system design, a pipeline review, a sample SQL exercise, or a paid task based on your real work.

NDA and IP assignment are completed first. Then we set up source access, warehouse or lakehouse access, orchestration jobs, dbt project context, data contracts, quality checks, BI dependencies, sample failure history, and the first pipeline or model to stabilize so the engineer can contribute without a week of hand-holding.

By day 7, you should see a concrete proof point: a pipeline stabilized, a dbt model tested, a freshness check added, a backfill made safer, a query made faster, a lineage issue exposed, or a downstream reporting problem traced to its source. Progress is visible before the trial becomes a long commitment.

During the risk-free trial, you evaluate data modeling judgment, pipeline reliability, SQL clarity, orchestration habits, cost awareness, ownership communication, and ability to make data usable without fragile manual fixes. If the fit is wrong, we replace the engineer within 48 hours.

How hiring actually works.

No procurement cycle, no mystery shortlists. Six steps from first call to first shipped feature, with timelines you can defend to leadership.

Data Engineer Scoping Call

Data Engineer Shortlist

Interview for Data Engineer Fit

Onboard Into the Data Engineer Workflow

First Data Engineer Proof Point

Data Engineer Trial Check

Data Engineer: Engagement Options

Three transparent ways to engage. All rates are in USD and exclude taxes. No recruitment fees, no notice periods.

Pilot

AI-Ready Pipeline Build

$14,000

fixed

3 weeks, senior data engineer

→One end-to-end pipeline
→dbt models + tests
→Documentation + lineage
→Production handover

Talk to Sales

Embedded

Senior Data Engineer

$4,800

/mo

Full-time, 5–10+ years

→Owns your pipeline roadmap
→On-call + freshness SLAs
→Reviews internal data team
→Cost discipline built in

Talk to Sales

Data Pod

Data Eng + Analytics Eng

$8,800

/mo

Pair build, 3–6 months

→End-to-end data platform
→Lakehouse + dbt + Airflow
→Quality + lineage + alerting
→Cost-tuned warehouse

Talk to Sales

Where Data Engineers Create Leverage

From SMEs and scaling companies to enterprise teams. Same senior bar; different shape of engagement.

01.

Modern Data Warehouse

Create a trusted analytics foundation for product analytics, executive reporting, finance reporting, experimentation, customer health, and AI use cases. The work clarifies source ownership, metric grain, dimensional models, access patterns, and how teams know whether data is fresh enough to trust.

02.

ETL and ELT Modernization

Replace brittle scripts, cron jobs, spreadsheet exports, and manual SQL handoffs with observable, tested, version-controlled workflows. The outcome is a pipeline that can be retried, backfilled, reviewed, and extended without one engineer being the only person who understands it.

03.

Streaming Data Systems

Process events from apps, services, devices, billing systems, marketing tools, product telemetry, and operational systems using Kafka, Kinesis, Pub/Sub, Spark, Flink, or managed streaming services. The design should handle ordering, late events, replay, retention, and downstream consumers.

04.

Data Platform Cleanup

Consolidate duplicated sources, standardize schemas, document contracts, retire unused tables, reduce warehouse waste, and improve ownership across core datasets so teams stop debating which table is correct.

What should change after you hire Data Engineers

A CTO is not hiring Data Engineers for activity, resumes, or another vendor dashboard. The hire has to create a visible business outcome, reduce delivery risk, and leave your internal team with a stronger system than before. This section defines the outcome we expect the engagement to prove.

Outcome 01 Reliable data flow into the decisions that matter

The first meaningful outcome is dependable data movement from source systems into the tables, features, dashboards, and AI workflows your company uses every day. That may mean a modern warehouse model for revenue and product analytics, an ELT pipeline replacing manual scripts, a streaming path for product events, a CDC feed from operational databases, or a lakehouse layer for AI-ready datasets. The Data Engineer should define source contracts, table grain, orchestration, retries, freshness targets, quality checks, ownership, and downstream dependencies so the system can be operated after the engagement.

Evidence to expect: a pipeline or model improvement with freshness notes, test results, lineage risks, downstream impact, and a clear owner for the next change

Outcome 02 Silent data failures are surfaced before they hurt the business

The biggest Data Engineer hiring risk is not a failed job that everybody sees. It is a quiet failure: duplicated records, delayed events, a changed source column, stale materializations, a broken join, a late backfill, a null spike, a cost spike, or a metric definition that changed without review. We reduce that risk with idempotent pipeline design, source freshness checks, dbt data tests, schema drift handling, observability, alert routing, lineage, backfill plans, and recovery runbooks. The goal is to make failure obvious, bounded, and fixable.

Evidence to expect: known failure modes, alert thresholds, test coverage, recovery steps, and review notes your technical lead can inspect

Outcome 03 Data reliability metrics a CTO can inspect

The engagement should be judged by operational data metrics, not activity. Useful inspection points include freshness by source and model, pipeline success rate, retry and failure patterns, data-test pass rate, duplicate rate, null rate, referential integrity, schema-change incidents, lineage coverage, query runtime, warehouse spend, backfill duration, dashboard incident count, and whether trusted datasets have clear owners. These signals let CTOs, product leaders, operators, finance teams, and AI teams see whether trust is improving.

Evidence to expect: a reliability snapshot with metric definitions, failing examples, cost notes, downstream impact, and a recommendation on what should change next

Outcome 04 Data platform knowledge your team keeps

A strong engagement should leave behind reusable operating assets, not only fixed jobs. That includes source inventories, contract notes, table ownership, model documentation, test definitions, lineage diagrams, orchestration rules, backfill steps, incident runbooks, cost-control decisions, access boundaries, and data retention assumptions. Your team should understand how to add a source, change a metric, rerun a pipeline, investigate a failed test, and decide whether a dataset is safe for BI or AI use.

Evidence to expect: architecture notes, dbt or model docs, data contracts, runbooks, decision records, and handover material your team can maintain

How to decide if Devlyn is the right partner for Data Engineers

Choose us when

You need a Data Engineer who can join a live product, work with your existing team, and create a specific outcome without months of recruiting or unmanaged freelance risk.

Interview for

Use the interview to test data modeling, ETL and ELT pipelines, warehouse design, orchestration, data quality, lineage, and cost-aware processing. Ask how the engineer would handle late events, schema drift, idempotent loads, failed backfills, slow queries, broken dbt tests, and conflicting metric definitions.

Expect clarity on

Scope, ownership, review cadence, communication rhythm, source-code access, warehouse access, source-system access, data sensitivity, IP assignment, security constraints, timezone overlap, and what proof should exist by day 7.

Do not accept

A generic shortlist, vague seniority claims, unclear pricing, weak code review process, or a vendor who cannot explain how the Data Engineer scope will be governed after onboarding.

Delivery governance and risk control

Devlyn is positioned as a senior AI and software engineering partner, not a resume marketplace. You get structured onboarding, secure access, NDA and IP assignment support, communication overlap, replacement flexibility, and delivery governance built around the outcome you are hiring for.

For a Data Engineer engagement, governance means source ownership, data contracts, table grain, model docs, orchestration rules, quality checks, lineage, retention, access control, and backfill policy stay visible. Your team should know which sources feed a metric, which models are trusted, which tests block release, which alerts require action, and which datasets are safe for BI, experimentation, finance, operations, or AI workflows.

We also align the work with practical controls for production data systems: scoped access to sensitive data, documented transformations, traceable model changes, clear ownership, incident notes, rollback or replay paths, and runbooks for rerunning jobs safely. This matters because data problems often look like product, finance, ML, or customer-success problems until the pipeline is finally inspected.

Ready to Hire a Data Engineer?

Share your sources, warehouse, orchestration tools, BI pain, AI data needs, reliability gaps, and cost constraints. We will shortlist Data Engineers who can build, monitor, and scale the systems behind trusted analytics and AI.

Hire Data Engineer Now Book a Free Consultation

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Frequently Asked Questions

Answers for CTOs, engineering leaders, product leaders, operators, and hiring managers comparing senior engineering capacity, delivery models, risk controls, and long-term ownership.

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your product, sources, warehouse or lakehouse, orchestration tools, timeline, and seniority needs. The goal is not to send resumes quickly; it is to send Data Engineers who match the outcome, risk profile, and communication bar for the role.

Yes. You interview the shortlisted engineers before committing. We recommend using the interview to test data modeling, ETL and ELT pipelines, warehouse design, orchestration, data quality, lineage, and cost-aware processing. A strong interview should include how the engineer handles schema drift, late events, failed jobs, slow queries, backfills, and conflicting metric definitions.

The first week should produce visible proof that the engineer understands your system and can move real work forward. For this role, you should see a pipeline or model improvement with freshness notes, test results, lineage risks, query or cost findings, backfill notes, and downstream impact. If progress is unclear, you should know that early, not after a long contract cycle.

A strong hire should produce data pipelines and models with clear contracts, orchestration, quality checks, lineage, freshness, and warehouse performance. The outcome should be measurable through freshness by source, pipeline success rate, data-test pass rate, duplicate rate, null rate, schema-change incidents, lineage coverage, query runtime, warehouse spend, and downstream trust.

Quality is managed through senior screening, role-specific interview criteria, code or architecture review, documented decisions, and delivery checkpoints. For Data Engineer work, we look for evidence across pipeline design, SQL modeling, orchestration, data contracts, dbt or equivalent tests, lineage, observability, backfill safety, access control, cost tuning, and handover. The engineer should be able to explain how a change affects upstream sources and downstream consumers.

Yes. The engineer joins your tools, repositories, standups, issue trackers, review process, BI tools, data catalog, warehouse, and communication channels. For Data Engineer work, we define the operating model explicitly: source ownership, data contracts, model docs, orchestration rules, access boundaries, and quality checks stay visible. This gives the role clear boundaries from the first sprint.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, reviews, and escalation. For Data Engineer engagements, the communication rhythm is tied to the proof points that matter: data freshness, pipeline reliability, quality-test pass rate, lineage coverage, query performance, warehouse cost, and downstream trust.

NDA and IP assignment are handled before onboarding. Access is scoped to the tools, repositories, datasets, systems, or environments required for the Data Engineer scope, and sensitive work is governed through your security rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer can handle data modeling, ETL and ELT pipelines, warehouse design, orchestration, data quality, lineage, cost-aware processing, and communication with your team. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one specialist, add adjacent roles, or move into a pod model depending on the scope. Common expansion paths include analytics engineering for metrics and dbt, backend engineering for event instrumentation, platform engineering for infrastructure, ML engineering for feature pipelines, security support for sensitive data access, and DevOps support for observability.

Typical options include AI-Ready Pipeline Build ($14,000 fixed scope) 3 weeks, senior data engineer, Senior Data Engineer ($4,800/mo) Full-time, 5–10+ years, Data Eng + Analytics Eng ($8,800/mo) Pair build, 3–6 months. We confirm the right model after discovery so you can compare dedicated hiring, a focused sprint, or a small pod against the risk and timeline of your actual Data Engineer requirement.

We can support both models. If you already have strong product and engineering leadership, the engineer can plug into your process; if you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around pipelines, dbt models, warehouse design, streaming systems, quality checks, lineage, freshness, and performance.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For Data Engineer hiring, that matters because the real risk is dashboards, ML features, finance reports, operations workflows, and customer teams depending on stale, undocumented, duplicated, or silently broken data. You get a shorter path to qualified candidates and a trial structure focused on technical outcomes rather than resume volume.

Devlyn is a better fit when the Data Engineer work affects production systems, customer workflows, AI workflows, finance reporting, security, cost, or long-term maintainability. You get vetting, replacement support, delivery governance, IP protection, and continuity around outcomes like data pipelines and models with clear contracts, orchestration, quality checks, lineage, freshness, and warehouse performance.

Data Engineers are a strong fit when your company needs trusted data movement, governed models, and reliable datasets. Common use cases include modern warehouse builds, ELT modernization, event tracking pipelines, CDC from product databases, billing and revenue data consolidation, customer health datasets, experimentation data, AI-ready feature tables, streaming analytics, data platform cleanup, and cost tuning for Snowflake, BigQuery, Redshift, Databricks, or cloud lakehouse systems. If the need is narrower, we can help you decide whether one specialist, a full-time dedicated engineer, or a small pod is the right model.

Hire Data Engineers Who Make Data Dependable

Why Companies Struggle to Hire Data Engineers

Why Hire Data Engineers from Devlyn