Data Engineering Services

Enabling the shift from fragmented data to scalable, AI-ready foundations

Struggling with data silos across systems and departments?

Disconnected ERP, CRM, marketing, and finance systems create fragmented insights. We unify these sources into a single data layer for consistent, cross-functional visibility and faster decision-making.

Is poor data quality affecting reports and AI outcomes?

Inaccurate or inconsistent data weakens reports, forecasts, and AI outputs. Our cleansing and validation frameworks elevate data quality to deliver clean, trusted, AI-ready datasets.

Legacy pipelines preventing you from accessing real-time data?

Outdated ETL/ELT pipelines slow down reporting and limit real-time access. We modernize data pipelines to ensure timely, reliable, real-time delivery for improved operational agility.

Data Engineering Services That Transform Your Data Landscape

Modern enterprises generate vast volumes of data, but without strong engineering foundations it stays fragmented, slow, and unreliable. Broken pipelines, inconsistent quality, and siloed systems delay decisions and limit value.

LevelShift's Azure-powered Data Engineering CoE delivers comprehensive Data Engineering Consulting Services and Solutions to modernize your data landscape with proven frameworks and accelerators—building a scalable, trusted data foundation for analytics and AI.

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Data Engineering Capabilities We Serve

Data Ingestion and Integration

Unify data from databases, applications, APIs, SaaS platforms, and streaming sources using robust ETL/ELT pipelines, CDC, and real-time integration—ensuring seamless connectivity across hybrid and multi-cloud environments.

Data Cleansing and Quality Management

Enhance data reliability with automated validation, de-duplication, profiling, anomaly detection, cataloging, metadata management, indexing, and authentication protocols to maintain clean, consistent, business-ready datasets.

Data Transformation and Processing

Convert raw data into analytics-ready formats through batch and real-time processing, business-rule enrichment, data modeling, vector embeddings, graph transformations, and indexing strategies optimized for BI, ML, and advanced analytics.

Data Storage and Architecture

Build scalable storage architectures using data lakes, warehouses, lakehouse platforms, Delta Lake, and vector storage—supported by partitioning, compression, and indexing to balance performance, cost, and scalability.

Orchestration and DataOps

Orchestrate and automate pipelines with DataOps practices, applying continuous monitoring, lineage tracking, metadata management, CI/CD workflows, and observability tools to ensure resilient, governed, and high-performing data operations.

Our Data Engineering Services

Data architecture and data engineering

We offer Azure-native data architectures built using Microsoft Fabric, Synapse, Azure SQL and ADLS. These environments form a scalable and secure foundation for analytics, reporting and AI-driven workloads.

ETL, data integration and analytics

Our services cover ETL and ELT pipeline development with Azure Data Factory and Synapse Pipelines. This enables unified data flows across applications and cloud systems, ensuring teams receive reliable, analysis-ready datasets.

Data warehousing and data lakehouse

We deliver Azure SQL Data Warehouse, Synapse and Fabric Lakehouse implementations that consolidate enterprise data, improve analytical performance, and support BI and advanced analytics at scale.

Data governance and management

Our governance offerings leverage Microsoft Purview and Delphix to implement cataloging, lineage tracking, access controls and quality rules—strengthening compliance, trust, and visibility across the data estate.

Data modernization, migration, and AIOps/DataOps/MLOps

We support modernization and migration to Azure through automated frameworks and operational practices. With Azure DevOps, DataOps, MLOps and AIOps, organizations gain dependable pipelines, faster deployments and efficient lifecycle management.

As a Microsoft Solution and Fabric Featured Partner, LevelShift can help you secure ECIF funding and take advantage of Microsoft Azure Consumption Commitment (MACC) for your PoC and implementation.

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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

Why LevelShift

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

Timelines vary based on data volume, complexity, and the number of source systems, but most Azure migrations fall in the 8–16-week range for mid-sized environments. Larger enterprise estates may require phased programs of 4–6 months. Costs typically depend on readiness assessment, re-engineering work, cloud provisioning, and testing requirements. LevelShift conducts upfront discovery to define scope, risks, and a clear migration roadmap so investments are predictable and aligned with business value.

Zero-downtime migration is achieved by using parallel environments, incremental syncs, and change data capture (CDC) until the final cutover. Azure Data Factory, Synapse Pipelines, and Fabric Dataflows support staged replication so source systems continue running without disruption. LevelShift uses controlled switchovers, rollback plans, and validation checkpoints to keep business operations uninterrupted during the transition.

Data Lake: Stores raw, large-scale structured and unstructured data; ideal for exploration, machine learning, and cost-efficient storage.
Data Warehouse: Holds structured, curated, analytics-ready data optimized for BI, reporting, and governed decision-making.
Data Lakehouse: Combines raw and curated data in a single architecture using technologies such as Delta Lake or Microsoft Fabric for governed, scalable analytics and AI.

LevelShift helps choose the right model based on reporting needs, AI goals, budget, and existing systems.

DataOps and MLOps automate deployment, monitoring, testing, and versioning of data and ML pipelines. This reduces manual rework, catches issues early, and shortens cycle times for updates or model retraining. Azure DevOps, Fabric, and Synapse integrations enable continuous integration and delivery, improving reliability and lowering operational overhead. The result is fewer failures, faster releases, and more predictable operational costs.

LevelShift uses Azure’s native security stack: RBAC, managed identities, encryption at rest/in transit, private endpoints, and network isolation —to secure data end-to-end. Compliance is strengthened through Microsoft Purview for cataloging, lineage, sensitivity labeling, and policy enforcement. Additional controls, such as audit trails, access governance, PII classification, and automated quality rules ensure that regulatory requirements and enterprise governance standards are consistently met.

Yes. LevelShift designs ETL and ELT pipelines using Azure Data Factory, Synapse, Fabric Dataflows, and Spark to handle both real-time and batch workloads. Whether the need is streaming ingestion, periodic refreshes, or high-volume transformations, pipelines are built to scale automatically, maintain quality, and deliver analytics-ready data for BI, AI, and operational use cases.

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