.jpg)
Nearly every organization today calls itself “data-driven.” Yet many struggle with broken reports, sluggish dashboards, security risks, and scaling issues that stop insight-driven growth.
That’s where outsourced data engineers step in. An outsourced data engineer is a specialist who quickly rebuilds your data pipelines, unifies your systems, and turns chaos into clarity.
This guide covers seven critical business problems these engineers solve fast, backed by real-world examples and current market data.
The High Cost of Broken Data: Why Businesses Outsource Data Engineering
Modern data teams often face a perfect storm of challenges:
- Fragmented tools and data silos across departments
- Performance bottlenecks that delay decision-making
- Overloaded in-house staff without time for architecture work
That’s why more enterprises are outsourcing data engineering to gain speed, scalability, and specialized expertise, apart from cutting costs.
The Big Data Engineering Services Market is projected to reach USD $91.54 billion in 2025 and nearly double to USD $187.19 billion by 2030. Meanwhile, the broader data engineering market is expected to grow 21% in jobs and hit USD $84 billion by 2025.
The reason is simple: internal hiring takes months, while outsourced data engineers can deploy in days with ready-made solutions.
7 Data Pain Points Outsourced Engineers Fix Fast
When you leverage the benefits of an outsourced data engineering team, they can help your organization quickly overcome common pain points such as:
1. Inconsistent or Broken Reports
The Problem: Finance, sales, and marketing teams each maintain their own spreadsheets and CRMs, leading to conflicting metrics and hours of manual reconciliation.
The Fix: Outsourced data engineers create a central warehouse that unifies all sources, defines single-source-of-truth metrics, and adds data quality rules into ETL pipelines.
Example: A retail chain’s CRM and ERP systems reported different quarterly revenues. Engineers built a validated “revenue metric” pipeline, reducing report discrepancies to zero and saving 40 hours per month in manual reconciliation.
2. Slow or Crashing Dashboards
The Problem: Dashboards take minutes to load or timeout entirely, frustrating users and delaying decisions.
The Fix: Engineers redesign data models, partition large tables, and migrate to high-performance cloud warehouses like Snowflake or BigQuery. They add query caching and clustering to cut latency by up to 95%.
Example: A financial dashboard querying billions of rows was optimized using date-based partitioning, reducing load time from 5 minutes to just a few seconds.
3. Data Silos and No Single Source of Truth
The Problem: Critical information lives in disparate systems, CRM, marketing platforms, ERP, and legacy databases, making analysis incomplete and time-consuming.
The Fix: Outsourced engineers build data ingestion pipelines and APIs that extract data from multiple sources and merge them into a central repository.
Example: A B2B company combined customer data from Salesforce and HubSpot with usage logs from its app to create a 360-degree customer profile. The result: an increase in cross-sell accuracy for sales teams.
4. Unreliable or Non-Existent Forecasting
The Problem: Spreadsheets with stale or incomplete data drive forecasting and budgeting, leading to costly mistakes.
The Fix: Engineers automate data collection and feed cleaned historical data into forecasting and ML models. They ensure data freshness, accuracy, and traceability.
Example: A consumer goods brand built a real-time demand forecast pipeline that pulled warehouse data every hour, reducing stockouts.
5. Compliance and Security Vulnerabilities
The Problem: Unsecured data storage and manual access control create risk under laws like GDPR and CCPA.
The Fix: Outsourced engineers implement role-based access, encryption, and audit logging while building pipelines. They apply security-by-design principles and monitor compliance continuously.
Example: A financial services client implemented data masking and tokenization so only authorized users could see PII. Result: zero compliance incidents in audit reports.
According to an IBM report, the U.S. alone breaches an average of USD $10.22 million. Embedding security early in data pipelines prevents these losses.
6. Excessive Manual “Data Janitor” Work
The Problem: Analysts and data scientists spend 50–80% of their time cleaning and wrangling data instead of building models and insights.
The Fix: Engineers automate data preparation with ETL/ELT pipelines that handle deduplication, type mismatches, and data validation automatically.
Example: An outsourced team implemented an Airflow workflow to automate data cleaning and joins from multiple APIs, reducing manual analyst work.
7. Inability to Scale With Business Growth
The Problem: As data volume and velocity explode, legacy systems can’t keep up. Jobs fail, queries slow, and costs skyrocket.
The Fix: Outsourced data engineers migrate to cloud-native architectures like modern lakehouses and streaming pipelines that scale elastically.
Example: A fintech startup moved from on-prem PostgreSQL to Databricks + Delta Lake for real-time processing. It cut pipeline failures and processing time from hours to minutes.
Why Choose Outsourcing for Data Engineering?
The key reasons to choose outsourcing for data engineering include its speed-to-value advantage and the flexibility of experts to handle whatever needs arise for your team.
The Speed-to-Value Advantage
Hiring senior data engineers can take 3–6 months. Outsourced teams deploy in days and start producing results immediately. They bring standardized playbooks and battle-tested architectures.
Flexibility and Focused Expertise
Need Snowflake optimization this month and Spark streaming next? Outsourcing lets you tap exact skills on demand without long-term contracts. Your core team stays focused on business objectives while specialists handle infrastructure.
How JADA Squad Provides Rapid Data Solutions
Expertise in Cloud and Modern Data Stack
Our engineers are skilled in Snowflake, Databricks, BigQuery, Kafka, Airflow, and dbt, building data systems that are fast, secure, and scalable.
Focus on Knowledge Transfer
Every engagement includes documentation and paired handoffs so your internal team can own the solution post-delivery.
Flexible Engagement Models
- Staff Augmentation: Add specialists to your existing team.
- Project Delivery: Define scope and milestones for turnkey execution.
- Hybrid Model: Combine both for speed and control.
The agentic AI for data engineering market is forecast to grow from USD $2.7 billion in 2024 to USD $66.7 billion by 2034, and JADA is already integrating these technologies into our solutions.
If you’re dealing with broken reports, slow dashboards, or unscalable systems, outsourcing your data engineering function is the fastest way to see results.
Ready to fix your data problems and accelerate insights? Contact The JADA Squad to scope a pilot and augment your data engineering team today.
Frequently Asked Questions
How long does it take an outsourced team to fix a broken pipeline?
Most issues are stabilized within 2 to 6 weeks, depending on complexity and system access.
How do we maintain data quality long-term?
By embedding data observability, freshness checks, lineage tracking, and alerting into pipelines, anomalies are caught before they reach dashboards.
What’s the difference between a data warehouse and a data lake?
Warehouses handle structured, query-optimized data; lakes store raw and semi-structured data. Modern lakehouses blend both scalability and speed.
How does data observability prevent customer impact?
It detects schema changes, delays, or anomalies in real time, alerting teams before bad data reaches reports.
Why outsource data engineering instead of data entry?
Because engineering drives strategic value, fixing systems, not just inputs, to unlock analytics and AI.
What is ETL in data engineering?
ETL (Extract, Transform, Load) is the process of ingesting raw data, cleaning and shaping it, then loading it into a warehouse for use in analytics and machine learning.
