What are data engineering services?

Data engineering services design, build and run the systems that collect, clean, store and deliver data for analytics, reporting and AI. They include ingestion pipelines from databases, SaaS apps and APIs, ETL and ELT processes, cloud data warehouses, data lakes and lakehouses, real-time streaming, data modeling, data quality monitoring and governance. Without solid data engineering, dashboards show conflicting numbers and machine learning models fail in production. A good data engineering company builds a platform that is reliable, secure, well documented and affordable to run as data volumes grow.

How much do data engineering services cost?

A focused pipeline or warehouse setup usually costs $10,000 to $35,000, for example connecting five to ten sources into Snowflake or BigQuery with modeled tables for reporting. A full data platform with many sources, streaming, data quality monitoring and governance typically ranges from $40,000 to $150,000. The main cost drivers are the number and complexity of sources, data volumes, real-time requirements, compliance rules and the state of existing data. Cloud costs for storage and compute are separate, and we estimate them upfront so there are no surprises.

How long does it take to build a data platform?

A first set of pipelines and a cloud warehouse can be live in 4 to 8 weeks, including assessment, architecture, ingestion of priority sources and modeled tables ready for dashboards. Enterprise data platforms with dozens of sources, streaming and governance usually take 3 to 6 months, delivered in phases by use case so the business sees value early. Timelines depend on source system access, data quality, security reviews and how quickly business owners can confirm definitions. Migrating legacy ETL tools can add time, which we plan for during the assessment.

What is the difference between ETL and ELT?

ETL (extract, transform, load) transforms data before loading it into the warehouse, which suits strict data privacy rules or limited warehouse compute. ELT (extract, load, transform) loads raw data first and transforms it inside the warehouse, which is usually faster to build, easier to change and better suited to modern cloud platforms like Snowflake, BigQuery and Databricks. Most of our data engineering services now use ELT with dbt, keeping a raw copy for auditing and reprocessing. We still use ETL where sensitive fields must be masked before they land.

Which tools does your data engineering team use?

Our data engineering team uses Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse and Microsoft Fabric for storage and compute; Apache Spark and dbt for transformation; Kafka, Kinesis and Pub/Sub for streaming; Airflow, Dagster and cloud schedulers for orchestration; and Fivetran, Airbyte and Debezium for ingestion. We write pipelines in Python and SQL and define infrastructure with Terraform. Native AWS, Azure and Google Cloud data services are used where they offer better value. Tool choices are always matched to your use cases and team skills.

Why choose Webshark as your data engineering company?

Webshark is a data engineering company with 7+ years of delivery and 580+ projects completed under ISO 27001 and CMMI Level 3 certified processes. Our data engineers work alongside our BI, software and AI teams, so data platforms connect cleanly to dashboards, applications and machine learning models instead of becoming isolated projects. We focus on reliability, documentation and cost control, not just getting data moving. Our team overlaps with US business hours for working sessions and incident response, and we support clients across 18+ countries.

Can you prepare our data for AI and RAG?

Yes. We build pipelines that extract text from PDFs, emails and knowledge bases, clean and chunk documents, create embeddings and load vector databases such as Pinecone, Weaviate, pgvector or OpenSearch for retrieval augmented generation (RAG). We keep indexes fresh as documents change and apply access controls so users only retrieve content they are allowed to see. For traditional machine learning we build feature pipelines and feature stores with consistent training and serving data. See our LLM development page.

How do you ensure data quality?

We treat data quality as code. Automated tests check freshness, row volumes, nulls, duplicates, accepted values, referential integrity and business rules on every pipeline run. Column-level lineage shows which reports and models depend on each field, so the impact of a broken source is clear immediately. Alerts go to named owners with context, and a data health dashboard tracks incidents over time. We also add data contracts with source system teams so schema changes are agreed before they break downstream data engineering pipelines.

Which technologies do you use?

For data engineering we mainly use Python and SQL, with Scala or Java for some Spark and streaming workloads. Platforms include Snowflake, Databricks, BigQuery, Redshift and Azure data services, orchestrated with Airflow or Dagster and deployed through Terraform and CI/CD on AWS, Azure or Google Cloud. Where data products need an interface, our wider team builds APIs with Node.js, Java Spring Boot or .NET and internal portals with React or Next.js. We recommend technologies your own engineers can maintain and hire for easily.

Who owns the source code and IP?

You do. All pipeline code, dbt models, SQL, orchestration DAGs, infrastructure as code, documentation and data catalogs created during your data engineering project belong to you, and IP ownership is set out clearly in the contract. Cloud and tool accounts are created in your name, so your data always stays in your environment. We sign NDAs before discovery starts, follow least-privilege access during the project and hand over all credentials and runbooks at the end. There is no vendor lock-in.

Can I hire a dedicated team instead of a fixed-price project?

Yes. Instead of a fixed-price project you can hire dedicated data engineers, analytics engineers and data architects on monthly contracts, from one engineer to a full team with a project manager and QA. This model suits companies with a growing list of data sources, ongoing analytics requests or long-running platform migrations. Engineers work in your repositories and tools, join your standups and follow your priorities, and you can scale the team with notice. Many clients start with a fixed-price assessment and pilot, then continue with dedicated engineers. See our hire full-stack developers page.

Do you provide support after launch?

Yes. After launch our data platform support covers pipeline monitoring, fixing failed loads, adapting to source system and schema changes, performance and cost tuning, security patches, new sources and new data models. Response times are agreed upfront by severity, so a broken finance pipeline at month end is handled first. Our team overlaps with US business hours for planned work and reviews. You receive monthly reports on pipeline reliability, data quality incidents and cloud spend, and we can train your team to take over routine operations.

TRUSTED BY DATA-DRIVEN BUSINESSES IN 18+ COUNTRIES

Clean, reliable, well-modelled data for analytics, reporting and AI

Data Engineering Services and Consulting

Batch and real-time data pipelines from apps, databases, APIs and SaaS tools.
Warehouses and lakehouses on Snowflake, Databricks, BigQuery, Redshift or Azure.
Data quality checks, lineage and governance your auditors can follow.
AI-ready data for machine learning, RAG and analytics.

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Data Engineering Services for Reliable, AI-Ready Data

Webshark provides data engineering services for companies whose data is spread across apps, databases, spreadsheets and SaaS tools. Our data engineering company designs and builds the pipelines, warehouses and lakehouses that bring that data together, clean it, model it and deliver it on time to dashboards, products and AI models. We work with Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, Apache Spark, Kafka, Airflow and dbt, choosing tools that fit your scale and budget. Every pipeline includes monitoring, data quality checks and documentation under ISO 27001 certified processes. Want dashboards on top? See our business intelligence services.

Years of Software Engineering Experience

580+

Projects Delivered Across Industries

18+

Countries Served, Including the USA

280+

Active Clients Trusting Our Engineering

Data Engineering Services Built Around Security and Compliance

Every data engineering engagement follows ISO 27001 certified security and CMMI Level 3 delivery processes, with data handling aligned to GDPR, CCPA, HIPAA and the industry standards your auditors expect.

GDPR

CCPA

HIPAA

DPDP Act, 2023

PCI DSS

PSD2

EMV & 3DS

ISO/IEC 9001

ISO/IEC 27018

SOC 2 Type II

FISMA

NIST Cybersecurity Framework

ISO 13485

PIPEDA

ISO/IEC 27001

OWASP

W3C

Data Engineering Services We Offer

From a single pipeline to an enterprise data platform, our data engineering services cover architecture, build, quality and operations.

Data Engineering From Source Systems to Insights

We design data platforms that are reliable today and flexible enough for tomorrow's analytics and AI use cases.

Data Pipeline Development

Batch and incremental ELT pipelines with Airflow, dbt, Fivetran or custom code.

Data Warehouse Services

Modern warehouses on Snowflake, BigQuery, Redshift or Synapse with clean, documented models.

Data Lake and Lakehouse

Databricks and Delta Lake or cloud data lakes for large and unstructured data.

Real-Time Streaming

Kafka, Kinesis and Pub/Sub pipelines for live dashboards, alerts and event-driven apps.

Data Quality and Governance

Automated tests, lineage, cataloguing and access policies that keep data trustworthy.

AI and ML Data Pipelines

Feature stores, vector databases and RAG pipelines that prepare data for AI.

Why Choose Our Data Engineering Company

Data platforms only create value when the data is trusted, timely and easy to use. That is what we engineer for.

Reliability You Can Measure

We define data SLAs for freshness and completeness, add automated quality tests and alert on failures before users notice. Idempotent, well-tested pipelines recover cleanly from errors. Monitoring dashboards show pipeline health at a glance, so your team trusts the data instead of double-checking every report.

Cost-Efficient Architecture

Cloud data platforms can get expensive fast. We design incremental loads, partitioning, clustering and sensible compute sizing, and review costs monthly. Clients often cut warehouse spend while handling more data, because the platform is engineered for efficiency from the start.

How Our Data Engineering Services Work

A proven delivery approach takes you from scattered data to a governed, scalable data platform.

Data Assessment

Our data engineering services begin with a two to three week assessment. We inventory data sources, volumes, formats and owners, review existing pipelines, scripts and reports, and identify quality, latency and access issues. Interviews with analysts and business teams reveal which use cases matter most, from finance reporting to machine learning. You receive a current-state map, a target architecture, tool recommendations and a phased roadmap tied to measurable business outcomes.

Architecture and Design

Next we design a modern data stack covering ingestion, storage, transformation, serving and orchestration. Choices between a warehouse, data lake or lakehouse, and between batch and streaming, are made against your use cases, data volumes, team skills and budget, not trends. We estimate cloud running costs for each option. The deliverable is an architecture document with diagrams, naming conventions, environment strategy and security design that your data engineering team can follow for years.

Ingestion Pipelines

We connect databases, SaaS applications, APIs, files and event streams using managed connectors such as Fivetran or Airbyte, or custom Python code where no connector exists. Change data capture with tools like Debezium keeps large tables in sync without heavy full reloads, and Kafka or cloud streaming services handle real-time events. Schema changes are detected and handled without breaking downstream reports. You get reliable, monitored pipelines that load fresh data on the schedule each use case needs.

Transformation and Modelling

With dbt, Spark or Databricks we clean, join and model data into clearly documented layers, from raw and staging to curated business tables ready for BI and AI. Version control, peer code review and automated tests make every transformation reliable and easy to change. Business logic such as revenue recognition or customer definitions lives in one place, so every team uses the same numbers. You receive tested models, documentation and a data catalog your analysts can browse.

Data Quality and Observability

Automated tests check data freshness, volumes, nulls, duplicates, referential integrity and business rules on every run, using tools such as dbt tests, Great Expectations or Monte Carlo style monitoring. Column-level lineage shows where every field comes from and what breaks if a source changes. Alerts go to the right owner in Slack or Teams, and dashboards track data health over time, so issues are fixed before they reach executive reports or machine learning models.

Security and Compliance

We apply encryption at rest and in transit, role-based and attribute-based access, masking or tokenization of sensitive fields and full audit logging. Controls are aligned with GDPR, HIPAA, PCI DSS, SOC 2 or CCPA as needed, and data residency and retention rules are built into the platform design from the start. Personal data is classified and tagged automatically where possible, making access reviews and data subject requests far simpler for your compliance team.

DataOps and Support

Finally we bring software engineering discipline to data with CI/CD for pipelines and models, infrastructure as code, separate development and production environments, and clear runbooks for common failures. We monitor cloud spend and tune warehouse and cluster usage to keep costs predictable. Our team can operate and extend your data platform long term under agreed response times, or train your engineers through pairing and documentation so they can confidently take over.