Data Engineering Services

Scalable Data Foundations for AI-Driven Enterprises

Struggling with data silos across enterprise systems?

As enterprises expand across cloud, SaaS, and on-premises environments, data often becomes fragmented across platforms. We unify disconnected sources into a centralized, governed data layer that improves visibility and enables consistent analytics and AI outcomes.

Is poor data quality affecting reports and AI outcomes?

Inconsistent and duplicate data can impact reporting, forecasting, and AI performance. We implement validation, cleansing, standardization, and governance frameworks to deliver trusted, AI-ready datasets across enterprise systems and pipelines.

Legacy pipelines preventing access to real-time data?

Legacy pipelines often introduce delays, reliability issues, and limited scalability. We modernize batch and streaming pipelines with scalable architectures, automated monitoring, and resilient processing frameworks to support real-time insights.

Enterprise Data Engineering Solutions, Built for the Age of AI

According to Forrester’s Data Culture And Literacy Survey, more than one-quarter of global data and analytics employees estimate their organization loses more than $5 million annually due to poor data quality, and 7% say the loss is $25 million or more. Modern enterprises generate vast volumes of data — but without strong engineering foundations, it stays fragmented, slow, and unreliable. Broken pipelines, siloed systems, and poor data quality don't just slow analytics; they put AI investments at risk.

LevelShift's Data Engineering CoE helps enterprises close that gap — delivering data engineering consulting services and solutions built on Microsoft Fabric, Azure, and Databricks, with structured frameworks and accelerators that turn fragile data infrastructure into a scalable, trusted foundation for analytics and AI.

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Our Data Engineering Services

Data Architecture & Platform Engineering

We design scalable, cloud-native data architectures using Microsoft Fabric, Azure Synapse, ADLS, Databricks Lakehouse Platform, and Delta Lake to support enterprise analytics, AI, and real-time processing workloads. Our architectures are built for governance, performance, interoperability, and long-term scalability across hybrid and multi-cloud environments.

Data Ingestion, ETL/ELT & Integration

We build resilient ETL/ELT and data integration pipelines using Azure Data Factory, Synapse Pipelines, Databricks Workflows, Delta Live Tables (DLT), and Apache Spark. These pipelines unify structured, semi-structured, streaming, and SaaS data sources into trusted, analytics-ready datasets with support for CDC, orchestration, and automated transformations.

Real-Time & Streaming Data Engineering

We enable real-time data processing and event-driven architectures using Databricks Structured Streaming, Event Hubs, Kafka, and Spark Streaming. Our solutions support operational intelligence, IoT telemetry, fraud detection, monitoring, and near real-time analytics with scalable, low-latency processing frameworks.

Data Warehousing & Lakehouse Engineering

We implement modern data warehouses and lakehouse platforms using Microsoft Fabric, Synapse Analytics, Databricks Lakehouse, Delta Lake, and Azure SQL. Our solutions consolidate enterprise data into high-performance analytical environments optimized for BI, AI, self-service analytics, and large-scale reporting workloads.

Data Governance & Data Management

We strengthen governance, visibility, and compliance using Microsoft Purview, Unity Catalog, and Delphix. Our services include metadata management, lineage tracking, access controls, data quality frameworks, cataloging, and governance policies that improve trust, security, and discoverability across the enterprise data estate.

Data Modernization, Migration & DataOps

We modernize legacy data platforms, warehouses, and ETL ecosystems using automated migration frameworks and cloud-native operational practices. Leveraging Azure DevOps, Databricks Workflows, CI/CD pipelines, Infrastructure as Code (IaC), DataOps, and MLOps, we improve deployment velocity, pipeline reliability, observability, and operational efficiency across the data lifecycle.

Talk to the experts who build data foundations enterprises rely on.

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Kumar Vellore

MD - Data, Analytics
and EI

Jonathan Wilcox

Director - Enterprise Architecture

Implementation Roadmap

Discovery and Data Source Profiling

Architecture Design and Technology Stack Selection

Environment Provisioning and CI/CD Setup

Data Ingestion and ETL/ELT Pipeline Build

Storage Optimization and Query Performance Tuning

Production Deployment, DataOps and AIOps Enablement

Customer Impact

Medallion-Based Data Modernization for a Global Tire Manufacturer

For the world's largest tire manufacturer, fragmented partner data across 8 incompatible formats and no central reporting system created blind spots across a vast global distribution network. By building a unified, 3-layer data architecture on Azure Logic Apps, Azure Data Factory, and Databricks, surfaced through Power BI, partner sales and inventory data was consolidated into a single governed platform with self-service analytics at scale.

80% Improved decision-making

Real-time, accurate inventory and sales data enabled faster, more confident forecasting across the partner network.

50% Decrease in reporting costs

Consolidated 8 incompatible data formats into a single platform, empowering teams with self-service analytics and eliminating manual overhead.

1 source of truth for all partner data

A Bronze to Silver to Gold architecture standardized and curated partner structure and KPI data, replacing siloed, incompatible systems.

Why LevelShift

Azure and Databricks-powered data engineering frameworks and accelerators
Expertise in enterprise data platform, warehouse, and ETL modernization
100+ data engineering and modernization projects delivered
Specialized Partner for Azure Data Warehouse Migration

Where are you in your Data Transformation Journey?

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Data Modernization Your Path to Data Transformation

Our Perspectives

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FAQs

Azure Synapse is an integrated analytics service combining data warehousing, big data processing, and pipeline orchestration. Compared to alternatives: Databricks leads for Spark-based engineering, ML, and Delta Lake workloads; Snowflake excels in multi-cloud portability and automatic scaling; Microsoft Fabric extends Synapse with a unified lakehouse and real-time intelligence layer. Choice depends on workload type, ML maturity, and cloud footprint.

Core capabilities span five areas: data ingestion and integration (ETL/ELT, CDC, streaming via Databricks Auto Loader or ADF); data quality and cleansing (profiling, validation, Delta Live Tables); transformation and processing (Spark, dbt, batch and real-time); storage architecture (data lake, warehouse, lakehouse, Delta Lake); and orchestration and DataOps (CI/CD, lineage, monitoring via Databricks Workflows or Airflow).

LevelShift Data Engineering Consulting Services deliver comprehensive expertise across all five areas to help enterprises build scalable, reliable, and AI-ready data foundations.

Mid-sized migrations typically take 8–16 weeks; enterprise-scale programs run 4–6 months. Key cost drivers are schema re-engineering, legacy ETL refactoring (especially to Spark or dbt), cloud provisioning, and parallel-run testing. Upfront discovery and assessment through enterprise data engineering services help make timelines, resource planning, and overall investment more predictable.

A data lake stores raw, unstructured data cheaply for exploration and ML. A data warehouse holds curated, structured data optimized for BI and reporting. A lakehouse — such as Databricks on Delta Lake or Microsoft Fabric on OneLake — combines both: ACID transactions, schema enforcement, and ML support directly on lake storage. The right choice depends on governance maturity, AI goals, and query patterns.

By running source and target environments in parallel using CDC and incremental syncs until cutover. Databricks Auto Loader and Delta Live Tables support continuous incremental ingestion during migration. Controlled switchovers, rollback plans, and validation checkpoints ensure business continuity throughout.

They apply CI/CD, automated testing, versioning, and observability to data and ML pipelines — reducing manual rework and surfacing failures earlier. Databricks Unity Catalog, MLflow, and Workflows consolidate lineage, model versioning, and scheduling in one platform, lowering operational overhead and improving release velocity.

Yes. Databricks Structured Streaming processes real-time and batch data on the same Delta Lake tables, eliminating separate lambda architectures. ADF and Fabric Dataflows handle scheduled batch needs. Pipelines scale automatically and deliver analytics-ready data for BI, AI, and operational use cases from a single unified stack.

Ready to modernize your Data Engineering foundation for AI Innovation?

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