Enterprise-Grade Data Engineering Services
Turn your raw data into a strategic asset with our managed data engineering services. We build scalable ETL pipelines, secure data warehouses, and real-time analytics platforms.
From Chaos to Clarity
Stop Guessing. Start Knowing.
Data is your most valuable asset, but only if it's clean, accessible, and understandable. Our Data Engineering Services help you break down silos and build a single source of truth for your organization.
- Eliminate data silos
- Ensure data quality and trust
- Democratize access to insights
- Scale without performance bottlenecks
Our Capabilities
End-to-End Data Solutions
From ingestion to visualization, we handle the entire data lifecycle.
Data Pipelines & ETL/ELT
Build robust, automated pipelines to ingest data from any source. We use tools like Airbyte, Fivetran, and dbt to ensure data quality and timeliness.
Cloud Data Warehousing
Centralize your data in scalable, high-performance warehouses. We are experts in Snowflake, Google BigQuery, and Amazon Redshift.
Business Intelligence (BI)
Visualize your data with interactive dashboards. We design intuitive reports in Power BI, Tableau, and Looker for data-driven decisions.
Real-time Analytics
Process data as it happens. We build streaming architectures using Kafka and Spark for use cases like fraud detection and live monitoring.
Data Governance & Quality
Trust your data. We implement catalogs, lineage tracking, and automated quality checks to ensure compliance and accuracy.
AI & ML Readiness
Prepare your data for AI. We build feature stores and clean datasets to accelerate your Machine Learning and AI initiatives.
Why Partner with Salt?
We bring software engineering rigor to data infrastructure.
DataOps Culture
We treat data as code. We implement CI/CD, version control, and automated testing for your data pipelines to ensure reliability and prevent regressions.
Vendor Neutral
Whether it's Snowflake, Databricks, AWS, or GCP, we recommend the stack that fits your budget and use case, not just what's trending.
Business-First
We don't just move data; we ensure it answers business questions. Our data models are designed for consumption, insight, and real-world value.
How We Engage
From assessment to full management, we support your data journey.
Discovery
We audit your current data landscape and define KPIs.
Strategy
We design the architecture and select the right tool stack.
Implementation
We build the pipelines, warehouse, and initial dashboards.
Managed Services
We monitor pipelines and optimize performance 24/7.
Secure & Compliant Data
Data security is non-negotiable. We build governance into the fabric of your data platform, ensuring you meet regulatory requirements without slowing down access.
Data Privacy (GDPR/CCPA)
Automated PII masking and right-to-be-forgotten workflows.
Encryption Everywhere
AES-256 encryption for data at rest and TLS 1.3 for data in transit.
Access Control
Fine-grained Role-Based Access Control (RBAC) and audit logging.
Our Tech Stack
- Snowflake & BigQuery
- dbt (Data Build Tool)
- Airflow & Prefect
- Power BI & Tableau
Frequently Asked Questions
Common questions about our data engineering services.
What is the difference between Data Engineering and Data Science?
Data Engineering focuses on building the infrastructure and pipelines to collect, store, and clean data. Data Science focuses on analyzing that data to build models and derive insights. You need strong engineering before you can do effective science.
Which data warehouse do you recommend?
It depends on your existing stack and requirements. Snowflake is excellent for multi-cloud flexibility, BigQuery is ideal if you're on GCP, and Redshift integrates tightly with AWS. We help you evaluate and choose the best fit.
Do you support migration from legacy systems?
Yes, we specialize in migrating data from on-premise databases (Oracle, SQL Server) to modern cloud data warehouses. We ensure zero data loss and minimal downtime during the transition.
How do you ensure data security?
We implement encryption at rest and in transit, role-based access control (RBAC), and column-level security. We also ensure compliance with regulations like GDPR, CCPA, and HIPAA.
Can you help with real-time data processing?
Absolutely. We have experience building streaming pipelines using Apache Kafka, Amazon Kinesis, and Spark Streaming for low-latency use cases.
Ready to Unlock Your Data?
Get a free data architecture assessment and see how we can turn your data into a competitive advantage.
Expert Insights
Data Engineering Insights & Strategies
Discover how to transform raw data into actionable intelligence with modern engineering practices.
Table of Contents
What is Data Engineering?
Data Engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is the foundation upon which all data science, analytics, and AI initiatives are built. Without robust data engineering, data scientists spend 80% of their time cleaning data rather than analyzing it.
A core responsibility of Data Engineering Services is to build and maintain the "pipes" that move data from source systems (databases, APIs, logs) to a destination where it can be analyzed (data warehouses, data lakes).
This involves designing ETL (Extract, Transform, Load) or ELT pipelines, ensuring data quality, and managing the underlying infrastructure for reliability and performance.
The Modern Data Stack (MDS)
The Modern Data Stack represents a shift away from monolithic, on-premise solutions toward modular, cloud-native tools that are easy to set up and scale.
Key components of the MDS include:
- Cloud Data Warehouse: Centralized storage engines like Snowflake, Google BigQuery, or Amazon Redshift that separate compute from storage for infinite scalability.
- Data Ingestion: Tools like Fivetran or Airbyte that automate the extraction of data from hundreds of SaaS applications.
- Data Transformation: dbt (data build tool) has become the standard for transforming data within the warehouse using SQL, enabling software engineering best practices like version control and testing.
Our Platform Engineering teams help deploy and manage these stacks using Infrastructure as Code, ensuring a secure and reproducible environment.
Data Engineering vs. Data Science
While often conflated, Data Engineering and Data Science have distinct roles. Data Engineers are the architects and builders; they ensure data is available, reliable, and accessible. Data Scientists are the analysts and mathematicians; they use that data to build models and derive insights.
Think of it like a restaurant: Data Engineers are the kitchen staff who source the ingredients, prep the stations, and ensure the kitchen runs smoothly. Data Scientists are the chefs who combine those ingredients to create the final dish.
Successful organizations need both. However, hiring expensive data scientists before you have a solid data engineering foundation is a common mistake that leads to low ROI.
Business Intelligence & Analytics
Data Analytics and Business Intelligence (BI) are the visible layer of the data stack. This is where raw data is turned into visual stories that drive decision-making.
We specialize in:
- Data Visualization: Creating interactive dashboards in Power BI, Tableau, or Looker that allow business users to explore data self-service.
- Metric Definitions: Establishing a semantic layer to ensure that "Revenue" or "Churn" means the same thing across the entire organization.
- Real-time Analytics: Building streaming pipelines for use cases that require sub-second latency, such as fraud detection or live inventory management.
By combining strong engineering with intuitive design, we help you build a data-driven culture.
Why Managed Data Engineering Services?
Data pipelines are fragile. APIs change, schemas drift, and volumes spike. Maintaining a data platform requires constant vigilance.
With our Managed Data Engineering Services, you get:
- Reliability: We monitor your pipelines 24/7 and fix failures before they impact your dashboards.
- Scalability: We optimize your warehouse costs and performance as your data grows from gigabytes to petabytes.
- Expertise: Access a team of certified engineers across the entire stack (AWS, Azure, GCP, Snowflake, dbt) without the challenge of hiring full-time staff.
Ready to turn your data into a strategic asset? Contact us to discuss your data strategy.