Build enterprise-grade data platforms with Databricks, real-time pipelines, lakehouse architecture, data governance frameworks, and ML-ready environments. From siloed data and slow analytics to unified platforms enabling real-time insights and AI/ML workflows, we engineer data infrastructure that scales with your business.
Our comprehensive data engineering and analytics services cover the full data lifecycle, from ingestion and transformation to governance and ML enablement. We build scalable, reliable, observable data platforms that turn raw data into competitive advantage through faster insights and AI-ready infrastructure.
Design and implement modern data platforms using Databricks Lakehouse, Snowflake, or cloud-native data services (AWS, Azure, GCP). We architect unified platforms consolidating data warehouses, data lakes, and streaming infrastructure, enabling SQL analytics, Python/Spark processing, and ML workloads on single platform. Eliminate data silos, reduce infrastructure complexity, and accelerate time-to-insight through scalable, cost-effective architecture.
Build real-time data pipelines using Kafka, Spark Streaming, Flink, or cloud-native streaming services processing millions of events per second. We implement change data capture (CDC), stream processing, real-time aggregations, and streaming analytics, transforming batch-oriented analytics into continuous insights. Enable real-time dashboards, operational analytics, and instant alerting on business-critical metrics.
Establish comprehensive data governance including data catalogs, lineage tracking, quality monitoring, access controls, and compliance frameworks (GDPR, CCPA, SOC 2). We implement automated data quality checks, anomaly detection, schema validation, and data observability, ensuring trusted, compliant, high-quality data powering analytics and ML. Reduce data incidents by 80% through proactive quality monitoring.
Design and build scalable ETL/ELT pipelines using modern tools (dbt, Airflow, Prefect, Databricks Workflows) extracting data from diverse sources, transforming for analytics needs, and loading into target platforms. We implement incremental processing, dependency management, error handling, and monitoring, automating data movement from transactional systems to analytics platforms reliably and efficiently.
Build ML-ready data infrastructure including feature stores, model training pipelines, experiment tracking, model serving, and monitoring. We implement MLOps practices using MLflow, SageMaker, or Databricks ML, enabling data scientists to train, deploy, and monitor models in production. Accelerate ML development from months to weeks through standardized infrastructure and automated workflows.
Deploy analytics infrastructure and business intelligence tools (Tableau, Power BI, Looker, Metabase) connected to governed data platforms. We design semantic layers, build data marts, create reusable metrics, and establish self-service analytics capabilities, empowering business users to generate insights without data team bottlenecks. Enable organization-wide data-driven decision making.
We don't just move data around, we build data platforms as products. Our approach combines modern lakehouse architecture, DataOps practices, and data mesh principles to create scalable, self-service, governed data infrastructure that evolves with organizational needs.
We implement lakehouse architecture combining data lake flexibility with data warehouse performance, supporting SQL analytics, Python/Spark processing, and ML workloads on unified platform. This eliminates data duplication across warehouses and lakes, reduces infrastructure costs by 40-60%, simplifies architecture, and enables seamless analytics-to-ML workflows. Single platform means less data movement, faster insights, and easier governance compared to traditional fragmented data architecture.
We adopt DataOps principles applying DevOps practices to data pipelines, version controlling transformations, automating testing, implementing CI/CD for data workflows, and establishing observability. Every pipeline change is reviewed, tested in staging, and deployed automatically. Data quality tests run continuously. Pipeline failures alert immediately with automated diagnostics. This automation reduces data incidents by 80%, accelerates pipeline development, and ensures production data reliability.
Our AI agents monitor data pipelines detecting anomalies, schema drifts, quality issues, and performance degradations humans might miss. AI analyzes data patterns predicting pipeline failures, suggesting optimization opportunities, and automatically remediating common issues. This AI-augmented observability provides early warning for data quality problems, reduces mean time to detection (MTTD) from hours to minutes, and frees data engineers from manual monitoring to focus on platform evolution and new capabilities.
Our structured data platform implementation delivers value incrementally while building toward comprehensive, enterprise-grade data capabilities.
We begin by understanding your current data landscape, identifying pain points, mapping data sources, and designing target data architecture. Our team assesses existing infrastructure, interviews stakeholders, evaluates data quality, and creates a prioritized roadmap aligned with business objectives.
Key Activities:
Deliverable: Data assessment report, target architecture diagrams, implementation roadmap, technology recommendations
Establish data platform foundations including lakehouse infrastructure, orchestration tools, governance frameworks, and initial high-priority data pipelines. We set up environments, implement core infrastructure, onboard priority data sources, and deliver first analytics use cases.
Key Activities:
Deliverable: Operational data platform, initial pipelines ingesting priority data, first analytics dashboards, monitoring systems
Progressively expand data coverage, implement additional pipelines, build analytics capabilities, and establish self-service patterns. We onboard remaining data sources, create data marts, deploy BI tools, and enable business users for self-service analytics.
Key Activities:
Deliverable: Comprehensive data coverage, analytics-ready data marts, deployed BI tools, self-service capabilities, trained users
Build ML/AI infrastructure, implement advanced analytics, optimize platform performance, and establish DataOps maturity. We deploy MLOps platforms, create feature stores, implement data mesh patterns for large organizations, and continuously optimize cost and performance.
Key Activities:
Deliverable: ML-ready infrastructure, advanced analytics capabilities, optimized platform performance, mature DataOps practices
Our data engineers have deep expertise across modern data platforms, streaming technologies, orchestration tools, and analytics frameworks. From Databricks and Snowflake to Kafka and dbt, we deploy the right data technology stack for your infrastructure needs, all orchestrated through proven patterns and best practices.
Data Volume & Velocity
Processing gigabytes daily costs less than terabytes or petabytes. Real-time streaming pipelines processing millions of events per second require more infrastructure and engineering than batch pipelines running nightly. Higher volume and velocity = more compute, storage, and engineering complexity.
Data Source Diversity & Complexity
Integrating 5 standard databases costs less than connecting 50+ diverse sources (APIs, SaaS tools, legacy systems, event streams, files). More sources = more connectors, transformations, quality checks, and maintenance overhead.
Analytics & ML Requirements
Basic reporting dashboards cost less than advanced analytics (real-time, predictive, prescriptive) or production ML infrastructure (feature stores, training pipelines, model serving). More sophisticated analytics = more infrastructure and specialized data science engineering.
Data Governance & Compliance Needs
Basic data platforms cost less than comprehensive governance including data catalogs, lineage tracking, access policies, sensitive data discovery, and compliance automation (GDPR, HIPAA, SOC 2). Regulated industries require more governance engineering and tooling.
Organization Size & User Count
Supporting 10 analysts costs less than enterprise-wide self-service platforms serving hundreds of business users. More users = more semantic layers, data products, performance optimization, and support infrastructure.
Existing Infrastructure Maturity
Building data platforms from scratch requires more investment than modernizing existing warehouses or lakes with better tools and practices. Lower starting maturity = more foundational work.
These are general industry ranges to help you budget:
Basic Data Platform (Small Scale, Limited Use Cases)
Single data warehouse, batch pipelines, basic reporting for small team
Industry range: $15K - $40K/month
Standard Data Platform (Mid-Sized, Growing Analytics)
Lakehouse architecture, real-time pipelines, BI tools, growing self-service capabilities
Industry range: $40K - $100K/month
Enterprise Data Platform (Large Scale, Comprehensive)
Multi-cloud lakehouse, extensive pipelines, ML infrastructure, data mesh, enterprise governance
Industry range: $100K - $300K+/month
Data Platform Transformation Projects
Initial platform build, migration, comprehensive pipeline development
Industry range: $200K - $800K per project
Unlike data consultancies focused on expensive enterprise tools, we deliver modern lakehouse-based platforms using cost-effective technologies (Databricks, open-source tools, cloud-native services) at 40-60% lower cost than traditional data warehousing approaches, while delivering superior performance, flexibility, and ML-readiness.
What Makes Our Data Engineering Different:
Lakehouse Architecture – Unified platform reducing costs 40-60% vs. separate warehouses and lakes while enabling analytics-to-ML workflows
DataOps Automation – Pipeline CI/CD, automated testing, and observability reducing data incidents by 80% and accelerating development
AI-Powered Data Quality – Automated anomaly detection, schema validation, and quality monitoring ensuring 99.9% data reliability
ML-Ready Infrastructure – Feature stores, training pipelines, and model serving built-in from day one, not afterthoughts
Self-Service Enablement – Semantic layers, data products, and governed self-service reducing data team bottlenecks
Cloud Cost Optimization – Storage tiering, compute rightsizing, and caching strategies reducing cloud data costs 30-50%
Proven Lakehouse Patterns – Pre-built architecture templates accelerating implementation 3-5x faster than custom builds
Dedicated Data Engineering Team
Full-time data engineers, data architects, and analytics engineers building and operating your data platform.
Best for: Organizations building comprehensive data platforms requiring sustained focus
Part-Time Data Specialists
Dedicated data engineers working part-time (20hrs/week) on pipelines, governance, and platform improvements.
Best for: Growing teams scaling data capabilities without full-time headcount
Augmented Data Engineers
Add individual data engineers, data architects, or ML engineers to your existing data team.
Best for: Teams with platform foundation needing specialized expertise (Databricks, streaming, MLOps)
Pricing depends on data volume, source complexity, and analytics requirements. View our transparent pricing models or get a custom data engineering quote after a consultation.
View Our Pricing Models or Get Custom Data Engineering Quote
From FinTech platforms building real-time fraud detection to healthcare organizations creating HIPAA-compliant data lakehouses, our data engineering services deliver analytics and ML infrastructure tailored to industry-specific requirements and compliance needs.
HIPAA-compliant data lakehouses unifying EHR systems, patient data, claims, and medical devices. Build secure data pipelines with audit trails, analytics for population health management, ML infrastructure for clinical decision support, and governance ensuring patient data privacy.
Real-time data platforms integrating transportation systems, warehouses, IoT sensors, and partner APIs. Build streaming analytics for shipment tracking, ML infrastructure for route optimization and demand forecasting, supply chain visibility dashboards, and operational analytics.
Unified data platforms combining transactional, behavioral, and supply chain data. Build real-time analytics for inventory optimization, customer segmentation and personalization engines, ML infrastructure for demand forecasting, and omnichannel analytics supporting seamless customer experiences.
Product analytics platforms processing usage telemetry, application logs, and customer data. Implement real-time user behavior analytics, ML infrastructure for churn prediction and feature recommendations, data products enabling customer-facing analytics, and multi-tenant data architecture.
Data platforms consolidating network telemetry, customer interactions, billing, and usage data. Implement real-time analytics for network operations, ML infrastructure for predictive maintenance and customer churn prevention, and data governance supporting telecommunications regulatory compliance.
Data platforms consolidating policy, claims, underwriting, and customer data. Implement real-time analytics for claims processing, predictive models for risk assessment and fraud detection, actuarial data pipelines, and governance supporting regulatory compliance across jurisdictions.
Enterprise data platforms consolidating core banking data, customer interactions, and market feeds. Implement SOX-compliant governance, real-time analytics for trading operations, customer 360 views, regulatory reporting automation, and ML infrastructure for personalization and risk management.
Real-time data platforms for fraud detection, risk analytics, transaction monitoring, and regulatory reporting. Build PCI DSS-compliant lakehouses, streaming pipelines processing payment events, ML infrastructure for credit scoring, and compliance-ready data governance meeting financial regulatory requirements.
Data Warehouse (Snowflake, BigQuery, Redshift) stores structured data optimized for SQL analytics, fast for business intelligence but expensive, limited to structured data, and not ML-friendly. Data Lake (S3, ADLS) stores any data type cheaply but requires complex engineering for analytics, flexible but poor performance and governance challenges. Lakehouse (Databricks, Delta Lake) combines both: cheap storage like lakes plus fast SQL analytics like warehouses, supporting structured and unstructured data with ML integration. We recommend lakehouse for modern platforms, it's 40-60% cheaper than warehouses while more performant than lakes.
Basic platform (single data warehouse, batch pipelines, basic reporting) deploys in 1-2 months. Standard lakehouse (real-time pipelines, BI tools, governance) takes 3-4 months. Enterprise platform (comprehensive pipelines, ML infrastructure, data mesh) requires 6-12 months. We deliver incrementally, first analytics use cases go live within 4-6 weeks while comprehensive platform builds progressively. Most organizations see immediate value from initial pipelines while continuing to expand platform capabilities over time.
Yes. We specialize in data platform modernization migrating from traditional warehouses (Oracle, Teradata, SQL Server) to modern lakehouses (Databricks, Snowflake). Migration strategies include: Parallel run (build lakehouse alongside warehouse, gradually migrate workloads), Phased migration (move pipelines and reports systematically), Hybrid approach (keep warehouse for specific workloads, lakehouse for analytics and ML). Most migrations complete in 6-12 months with zero downtime for critical analytics.
Perfect starting point. Many organizations evolve from spreadsheets to proper data platforms. We implement: Source system integration replacing manual exports with automated pipelines, Data consolidation unifying spreadsheet data into governed platforms, Self-service analytics giving users BI tools instead of spreadsheets, Gradual migration starting with critical reports while others transition progressively. Within 2-3 months, most critical spreadsheet-based analytics move to automated, reliable data platforms.
Not initially. Our data engineering teams provide required expertise while progressively training your engineers through hands-on collaboration and knowledge transfer. By engagement end, your team has practical data platform experience. However, long-term platform success benefits from internal data engineering capability, either hiring data engineers or upskilling software engineers into platform roles. We support both approaches: fully managed platforms or capability building for self-sufficiency.
Comprehensive data quality framework including: Schema validation ensuring data structure consistency, Automated quality checks validating completeness, accuracy, uniqueness, Anomaly detection using AI to identify unusual patterns, Data lineage tracking data flow from source to analytics, Pipeline monitoring alerting on failures and performance degradation, Data observability providing visibility into data health. This reduces data quality incidents by 80% and mean time to detection from hours to minutes.
Yes. We integrate with virtually any data source: Databases (PostgreSQL, MySQL, SQL Server, Oracle, MongoDB), SaaS applications (Salesforce, HubSpot, Stripe, Google Analytics), Event streams (Kafka, Kinesis, Pub/Sub), APIs and webhooks, File systems (S3, Azure Blob, SFTP), Legacy systems, and Custom applications. Modern data platforms excel at heterogeneous source integration, we've connected hundreds of different systems to unified platforms.
Hiring in-house requires 3-6 months recruitment, $150K-$250K/year per engineer, ongoing training, and tool licensing, plus risk of turnover losing expertise. Our data engineering teams deploy in <3 weeks, cost 40-60% less than US/UK hires, bring proven platform expertise, include multiple specializations (pipelines, ML, governance), and scale up/down based on needs. Most clients use our teams during platform build, then transition to smaller internal teams for ongoing operation, or continue with managed platform services.
We track metrics through SEOP dashboards: Time to insight (how quickly business questions get answered), Data quality (completeness, accuracy, freshness scores), Pipeline reliability (uptime, failure rate, MTTR), Self-service adoption (users creating own reports vs. requesting from data team), ML training time (how quickly models train on new data), Cloud costs (cost per query, storage optimization), User satisfaction (internal NPS from business users). Quarterly reviews demonstrate ROI through faster analytics, reduced data team toil, and ML acceleration.
Post-implementation options include: Ongoing platform engineering where our team continues expanding pipelines, adding data sources, optimizing performance, and evolving platform capabilities. Managed data operations providing pipeline monitoring, incident response, and maintenance. Advisory services with part-time data architect providing strategic guidance on platform evolution. Knowledge transfer and handoff where we train your team for complete platform ownership. Most clients choose managed operations or advisory to maintain platform momentum.
Our data engineering and analytics services combine lakehouse architecture, real-time pipelines, and AI-powered data quality to transform fragmented data landscapes into unified, governed, ML-ready platforms, enabling 10x faster insights, 99.9% data reliability, and AI/ML capabilities without years of infrastructure investment.
OPERATED DELIVERY · 5 REGIONS · FIRST SPRINT IN 21 DAYS“Their willingness to go beyond what is required and technical expertise resulted in a world-class product that we are extremely proud to take to market.”