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Discipline

Data Engineering

Data engineers who build trusted pipelines, warehouses and streaming systems with governance baked in from day one.

Snowflake
BigQuery
Databricks
dbt
Airflow
Spark
Kafka
Fivetran
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Tailored consultant

Who you get on day one

Data engineers who ship reliable data products and embed AI tooling into the modern data stack.

Latest skills
SQL
Python
dbt
Snowflake
Airflow
Kafka
Spark
Certifications
  • Snowflake SnowPro
  • Databricks Certified Data Engineer
  • dbt Analytics Engineer
AI fluency
  • Uses LLM copilots for SQL/dbt authoring
  • Implements text-to-SQL grounded in semantic layers

Strategies & playbooks for Data Engineering

Concrete plays our consultants run to resolve the complex problems we see most often in this discipline.

01
Data product thinking
Problem

Pipelines owned by nobody; consumers can't trust the data.

The play

Treat each dataset as a product with owner, SLA, contract, docs and quality metrics.

Outcome

Trusted data, faster analytics, fewer 'what does this column mean' tickets.

02
ELT + dbt modeling
Problem

Spaghetti SQL in BI tools nobody can refactor.

The play

Land raw data, model in dbt with tests and docs, expose semantic layer to BI.

Outcome

Single source of truth; analyst velocity multiplies.

03
Streaming where it pays
Problem

Everything 'must be real-time'. but ops complexity kills value.

The play

Identify the few use cases where freshness drives revenue; use Kafka + Flink only there, batch elsewhere.

Outcome

Right tool per workload; ops cost contained.

AI-assisted approach

How AI accelerates Data Engineering

AI accelerates modeling, documentation and quality. and makes data accessible via natural language.

AI-assisted SQL & dbt

LLMs draft models and tests from schema + business intent; engineers review.

dbt Copilot
Cursor
GPT-5
Auto-documentation

Generate column descriptions, lineage narratives and onboarding docs from metadata.

Atlan AI
Select Star
Text-to-SQL for analysts

Natural-language queries grounded in semantic layer for self-serve analytics.

Cube
Vanna AI

Recommended tools we propose as consultants

Curated stack our consultants bring on day one. chosen for fit with your scale, team and existing investment.

Warehouse
  • Snowflake
    Separation of compute/storage with strong governance.
  • BigQuery
    Serverless and cost-effective for variable workloads.
  • Databricks
    Unified for BI + ML on lakehouse architecture.
Transformation
  • dbt
    SQL-first modeling with tests, docs and lineage.
  • SQLMesh
    Stronger versioning and virtual environments.
Streaming
  • Kafka
    Durable backbone for event-driven systems.
  • Flink
    Stateful stream processing with exactly-once semantics.
Primer

What this discipline really is

Data Engineering is the discipline of moving, modeling and serving trustworthy data so analysts, ML, and product surfaces can rely on it. Modern stacks (Snowflake, BigQuery, Databricks + dbt + Airflow/Dagster) make the plumbing easier. modeling, quality and governance are still the hard parts.

Bad data quietly poisons every dashboard, model and decision downstream.
Without lineage, ‘why is this number wrong?’ becomes a multi-day investigation.
Streaming unlocks use cases (fraud, personalization, ops) that batch can’t serve.
Governance and access control are now table stakes. not optional.

Key areas inside Data Engineering

1
Warehouse & lakehouse architecture

Snowflake, BigQuery, Databricks. picked and structured for your access patterns and cost profile.

Medallion architecture
Warehouse vs lakehouse
Cost optimization
2
Modeling & transformation

dbt-driven analytics engineering with tests, docs, lineage and clear ownership.

dbt
Dimensional modeling
Data Vault
SQLMesh
3
Orchestration

Airflow, Dagster, Prefect. DAGs that are observable, retryable and SLA-driven.

Airflow
Dagster
Asset-based pipelines
Backfills
4
Streaming & real-time

Kafka, Flink, Kinesis. for use cases where minutes-late kills value.

Kafka
Flink
CDC
Materialize
5
Governance & quality

Catalog, lineage, contracts, quality tests with circuit breakers and SLAs.

Data contracts
OpenLineage
Unity Catalog
Quality SLAs

Maturity model. where are you today?

Level 1. Ad-hoc

Spreadsheets and ad-hoc SQL, no central warehouse, conflicting numbers.

Level 2. Repeatable

Central warehouse, scheduled jobs, basic dashboards, no tests.

Level 3. Defined

dbt with tests & docs, orchestrator, catalog, SLAs on critical pipelines.

Level 4. Optimized

Data products with contracts, full lineage, real-time where needed, FinOps on data spend.

Best practices we apply

  • Treat data pipelines as products with owners, SLAs and on-call.
  • Test data like you test code. schema, freshness, volume, business rules.
  • Make lineage visible end-to-end; otherwise debugging scales linearly with data sources.
  • Adopt data contracts at the producer boundary; stop catching breakages downstream.
  • Track cost per pipeline; the most expensive query is rarely the most useful one.

Common pitfalls & how we fix them

‘One big script’ pipelines
Fix: Modularize in dbt or asset-based DAGs; small, testable units.
No tests on data
Fix: dbt tests + freshness + volume + custom business rules in CI.
Streaming for everything
Fix: Use streaming only when latency demands it; batch is cheaper and simpler.
Catalog that nobody updates
Fix: Auto-generate from dbt + lineage tools; make it the source of truth.

Outcomes you can expect

  • Trusted data products
  • Sub-hour pipeline SLAs
  • Analytics-ready warehouses
  • Governed, documented datasets

Engagement models

Warehouse build
Modern stack on Snowflake/BigQuery/Databricks with dbt models.
Streaming pipeline
Real-time ingestion and processing with Kafka and Flink/Spark.
Data governance
Catalog, lineage, quality and access controls across the platform.

KPIs we commit to

<1 hour
Pipeline SLA
>99%
Data quality pass
−50%
Time-to-insight
Optimized
Cost per TB

Tools & technologies

Warehouses
Snowflake
BigQuery
Databricks
Redshift
Modeling & transform
dbt
SQLMesh
Spark
Polars
Orchestration
Airflow
Dagster
Prefect
Streaming
Kafka
Flink
Kinesis
Pub/Sub
Ingest & governance
Fivetran
Airbyte
Unity Catalog
OpenLineage

What you get

  • Lakehouse / warehouse architecture
  • dbt project with tests, docs and lineage
  • Pipelines with SLAs and alerting
  • Streaming ingestion topology
  • Data catalog with ownership
  • Quality framework with circuit breakers

How we deliver

  1. 1
    Discovery
    Workshops to scope outcomes, constraints, success metrics and risks.
  2. 2
    Match
    Ranked consultants with score, availability and pre-vetted skills.
  3. 3
    Pre-onboarding
    Stack simulation aligns the consultant with your conventions before day one.
  4. 4
    Delivery
    Two-week cadence with transparent metrics, demos and async updates.
  5. 5
    Knowledge transfer
    Documentation, runbooks and pairing so capability stays in-house.

Roles available on the bench

RoleLevelIndicative rate
Data EngineerMid - SeniorFrom €550/day
Analytics EngineerSeniorFrom €600/day
Data ArchitectStaffFrom €850/day

Rates are indicative; final pricing depends on seniority, location and engagement length.

Common stack overlap

Python
SQL
Spark
Kafka
Terraform
AWS
GCP
Azure

Certifications on the bench

  • Snowflake SnowPro
  • Databricks Data Engineer Pro
  • GCP Professional Data Engineer
Case study

Media. real-time analytics platform

Problem

Batch pipelines delivered KPIs 24h late; ad ops decisions lagged.

Solution

Kafka + Flink streaming into Snowflake, dbt models, governed catalog and SLA monitoring.

Result

KPIs available with <5 min latency, ad-yield up 9% in first quarter.

Why teams choose Codivers

Pre-vetted consultants graded on skills, domain depth and soft skills.
Pre-onboarding simulation = day-one productive engineers.
Transparent scorecards, weekly health checks and replaceable on demand.
Senior bench across 8 disciplines. scale up or rebalance without re-hiring.

Glossary. speak the language

Lakehouse
Architecture combining data lake storage with warehouse-like ACID & SQL.
Medallion
Bronze (raw) → Silver (cleaned) → Gold (modeled) layered architecture.
Data contract
Schema + SLA agreement between data producer and consumer.
CDC
Change Data Capture. streaming row-level changes from a source DB.
dbt
Data build tool. SQL-based transformations with tests, docs and lineage.

Recommended reading

Fundamentals of Data Engineering (Reis, Housley)
Book
The canonical modern data engineering text.
The Data Warehouse Toolkit (Kimball)
Book
Dimensional modeling reference, still essential.
dbt Discourse. Best Practices
Reference
Living guide to analytics engineering with dbt.

Frequently asked

Do you cover analytics engineering?
Yes. dbt modeling, semantic layers and BI enablement.
Migration from legacy DWH?
Yes. Teradata, Oracle, on-prem Hadoop to cloud-native warehouses.

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