Established a unified customer sentiment platform that integrates social listening, CRM interactions, and network performance signals into a governed lakehouse for enterprise reporting.
Scope of Work
Lakehouse architecture, pipeline engineering, and executive analytics delivery
Business Challenge
Customer feedback was fragmented across high-volume, heterogeneous sources with no common data model. Batch-oriented processing introduced latency, and leadership lacked a validated layer for sentiment KPIs.
Our Approach
Designed and implemented scalable Databricks and PySpark pipelines integrating Sprinklr, LinkedIn, CRM, network telemetry, and external APIs into a centralized lakehouse. Delivered curated semantic models, Power BI executive dashboards, and automated pipeline monitoring for ongoing reliability.
Technology Stack
SprinklrAzure DatabricksPySparkDatabricks SQLAzure Data LakePower BI
Automated end-to-end machine learning workflows for computer-vision and search models — from dataset versioning through training, evaluation, and governed production deployment.
Scope of Work
MLOps architecture, CI/CD automation, and production inference optimization
Business Challenge
Model delivery lacked a repeatable path from dataset version to production. Inference cost and latency were not benchmarked, and experiment metadata was scattered across teams, limiting reproducibility and auditability.
Our Approach
Architected GitLab-driven SageMaker workflows with versioned datasets and hyperparameter tuning. Deployed TensorRT, Ray, and Triton inference on Docker and ECR, built EMR-to-SageMaker retraining pipelines, and established Aurora-backed metadata models with automated lineage tracking.
Delivered a streaming analytics platform that ingests rig telemetry from multiple vendor hubs into Databricks, enabling real-time on-bottom drilling performance visibility for field operations.
Scope of Work
Streaming architecture, IoT integration, and operational analytics
Business Challenge
High-frequency IoT telemetry arrived from multiple vendors without consistent schema governance. Historical EDR data in legacy systems needed alignment with live feeds to support unified operational reporting.
Our Approach
Built a real-time Databricks pipeline using PySpark and Delta Lake with medallion architecture (bronze, silver, gold zones), depth-based on-bottom drilling aggregation, and automated historical EDR backfilling through Azure Data Lake Storage.
Technology Stack
Azure DatabricksDelta LakeAzure IoT HubsPySparkAzure DevOpsPower BI
Capabilities Delivered
Data EngineeringInternet of Things (IoT)Cloud Enablement
Combined wellbore trajectory and formation-level data with drilling events to give subsurface and completions teams integrated geospatial analytics at scale.
Scope of Work
Geospatial data engineering and subsurface analytics
Business Challenge
Geospatial joins between well trajectories and geological formations were computationally expensive at scale, and dashboard performance degraded on large Delta tables.
Our Approach
Engineered geospatial PySpark models, segment aggregation pipelines by geological formation, and optimized Delta Lake storage and SQL query patterns to improve dashboard responsiveness and analytical depth.
Technology Stack
Azure DatabricksPySparkGIS datasetsDelta LakePower BI
Aggregated completion and frac pressure time-series data with automated anomaly detection to strengthen production analytics and operational decision-making.
Scope of Work
Time-series engineering, ML-ready pipelines, and CI/CD automation
Business Challenge
Multi-level time-series computations on completion data required significant manual effort. Pipeline deployment, monitoring, and reporting lacked standardized automation.
Our Approach
Automated medallion-layer pipelines, implemented PySpark and Delta upsert frameworks for pressure anomaly detection, and established Azure DevOps CI/CD for pipelines and Power BI semantic models.
Technology Stack
Azure DatabricksPySparkDelta LakeAzure DevOps CI/CDPower BI
Migrated lakehouse assets across landing, integration, and consumption zones to Unity Catalog, establishing enterprise-grade governance, lineage, and fine-grained access control.
Scope of Work
Data governance, access control, and platform migration
Business Challenge
Existing models and pipelines needed onboarding without disrupting downstream consumers. Schema consistency and permission management across development, test, and production environments was critical.
Our Approach
Delivered a migration framework with automated schema registration, table migration, permission synchronization, schema-drift detection, Azure AD-integrated security with data masking, and multi-environment CI/CD.
Consolidated SAP and non-SAP source systems onto Microsoft Fabric, enabling scalable analytics and modern Power BI reporting for global manufacturing operations.
Scope of Work
Platform modernization, legacy code conversion, and analytics enablement
Business Challenge
Critical business logic resided in legacy SAS and Oracle PL/SQL across heterogeneous sources. Client teams required a documented, maintainable platform with limited prior Fabric experience.
Our Approach
Led migration using Synapse, Databricks, and OneLake; converted SAS and PL/SQL to PySpark; implemented Qlik Replicate and Databricks ingestion pipelines feeding governed Power BI reports with comprehensive handover documentation.
Technology Stack
Microsoft FabricOneLakeAzure DatabricksAzure Data FactoryQlik ReplicatePower BI
Transformed a manual Excel-based part cross-reference process into a governed Microsoft Fabric platform with sub-second report generation and standardized multi-region deployments.
Scope of Work
Infrastructure-as-code provisioning and dashboard migration
Business Challenge
The existing Excel dashboard relied on manual refresh cycles and could not scale. Environment provisioning was inconsistent across regions and tenants, slowing delivery and increasing operational risk.
Our Approach
Provisioned Azure SQL, Synapse, and Data Factory using ARM and Bicep templates; migrated the dashboard to Fabric; re-platformed data to PostgreSQL with .NET reporting and structured enablement for client teams.
Technology Stack
Microsoft FabricAzure SynapsePostgreSQLARM templatesBicepPower BI
Built pricing analytics, budget forecasting, and Azure cost visibility for finance stakeholders, with proactive monitoring across the enterprise data platform.
Scope of Work
Financial analytics, cost monitoring, and platform auditing
Business Challenge
Synapse, Data Factory, and SQL DW workloads lacked centralized auditing and anomaly detection. Unmonitored cloud spend created budget overrun risk for platform operations.
Our Approach
Implemented Azure Monitor and Log Analytics for platform auditing, configured cost-management alerts, delivered real-time Power BI financial dashboards, and established scalable Synapse and OneLake pipelines with access-log audits.
Technology Stack
Azure SynapseMicrosoft FabricAzure MonitorLog AnalyticsPower BI
Migrated legacy SAS workloads to a secure, governed Azure platform with self-service Power BI and Tableau reporting for cross-functional business teams.
Scope of Work
Legacy modernization, security hardening, and self-service analytics
Business Challenge
SAS code required re-platforming for compatibility and performance in SQL DW. Sensitive data demanded secure, role-based access across multiple business functions.
Our Approach
Migrated SAS workloads to Azure SQL DW; configured Synapse with Azure AD and Privileged Identity Management; deployed ARM-based infrastructure-as-code; implemented diagnostic logging and activity monitoring.
Technology Stack
Azure SynapseAzure SQL DWAzure Data FactoryPythonPower BITableau
Executed a full-scale migration of a business data platform into Microsoft Fabric with automated, standardized integration workflows and governed reporting.
Scope of Work
End-to-end platform migration and integration automation
Business Challenge
The organization needed reliable, automated data integration on a new platform without disrupting existing business processes or reporting cadences.
Our Approach
Delivered a migration pipeline using Fabric Data Factory, OneLake, and Fabric Warehouse with DBT and Python transformations, automated testing, and Power BI reporting layers.
Modernized TD's financial reporting platform on Azure with Snowflake as the analytical warehouse, Databricks for ingestion, and continuous operational support for production workloads.
Scope of Work
Cloud migration, data quality engineering, and operational support
Business Challenge
Operational stability was required across Snowflake, DBT, Databricks, and Tableau. Business-rule consistency had to be maintained across raw, historical, and exception record layers under regulatory scrutiny.
Our Approach
Implemented DBT transformations with YAML unit tests; built Databricks ingestion and ETL pipelines; established multi-zone raw-to-curated-to-consumption architecture in Snowflake; and delivered Tableau semantic layers with validated metrics.
Technology Stack
SnowflakeAzure DatabricksDBTAzure Data FactoryTableau
Migrated legacy SAS ETL and analytics workloads to a Databricks lakehouse, improving scalability, performance, and long-term maintainability for financial reporting teams.
Scope of Work
Legacy analytics modernization and regulatory reconciliation
Business Challenge
Complex SAS programs required reverse-engineering and validation. Regulatory reconciliation between SAS and Databricks outputs was mandatory, alongside performance tuning on large financial datasets.
Our Approach
Translated SAS to Databricks SQL and PySpark; built lakehouse raw-to-curated models with Delta partitioning; delivered Tableau dashboards with validated metrics and documented reconciliation runbooks.
Technology Stack
Azure DatabricksPySparkDelta LakeAzure Data FactorySASTableau
Migrated IFDS data for Individual Wealth portfolios into AWS with strict client-scoped access controls and operational dashboards for wealth management teams.
Scope of Work
Cloud migration leadership and governed analytics delivery
Business Challenge
Multiple source systems and file formats needed consolidation under strict data-exposure rules per client and portfolio, with cross-functional delivery against fixed regulatory timelines.
Our Approach
Built ingestion pipelines into Redshift with IAM roles and row-level security; delivered Tableau dashboards for Individual Wealth Operations; and managed development, QA, and production rollout teams.
Technology Stack
IBM DataStageAWS RedshiftIAMPythonTableauSalesforce
Delivered enterprise-wide point-of-sale analytics across 500 retail locations, integrating data governance and master data management during a major POS platform migration.
Scope of Work
Enterprise analytics program leadership and data governance
Business Challenge
POS data from 500 stores arrived with inconsistent quality and formats. A distributed delivery team required coordinated architecture, governance, and remediation standards.
Our Approach
Led the ETL and analytics team building the POS500 platform on Azure with governance and MDM frameworks; implemented Azure Data Pipeline and SSIS flows with systematic data quality remediation.
Improved real-time inbound offer presentation for residential customers, increasing campaign effectiveness while reducing reliance on broad mass-marketing programs.
Scope of Work
Campaign analytics design and targeted offer orchestration
Business Challenge
Inbound campaign presentation success was below target at 74%. Campaign data was fragmented across Oracle and Teradata, limiting the ability to deliver personalized offers at the point of contact.
Our Approach
Designed IBM Unica inbound campaign workflows; built Cognos operational reports; and sourced and integrated new data assets in Oracle and Teradata to enable targeted, event-driven campaign management.
Technology Stack
IBM Unica CampaignUnica InteractCognosOracleTeradata
Built an enterprise campaign management platform enabling real-time loyalty programs, contextual marketing, and Next Best Offer capabilities across all customer touchpoints.
Scope of Work
Real-time campaign architecture and customer analytics
Business Challenge
The organization needed real-time personalization from high-volume CDR streams, multi-channel campaign orchestration, and reduced operational expenditure on legacy support contracts.
Our Approach
Delivered Customer 360 behavioural analytics, Next Best Offer propensity models, and fulfilment orchestration between the campaign platform and downstream operational systems.
Technology Stack
IBM Unica CampaignKnowesis SIFTSIFT OrchestratorCDR processing
Defined the cloud roadmap and migrated a 200 TB on-premises Teradata enterprise warehouse to AWS Redshift, supported by an executive OPEX and CAPEX business case.
Scope of Work
Cloud strategy, vendor evaluation, and large-scale EDW migration
Business Challenge
A 200 TB enterprise warehouse migration required vendor evaluation across major cloud providers and executive alignment on total cost of ownership and migration risk.
Our Approach
Developed an enterprise cloud roadmap with OPEX and CAPEX analysis; led RFP and RFQ processes with top vendors; executed Teradata-to-Redshift migration aligned with governance and master data management standards.
Technology Stack
TeradataAWS RedshiftBusiness ObjectsQlik SenseOracle Data Integrator
Delivered consolidated KPI visibility for sales and marketing leadership, unifying performance metrics across business units on a governed analytics platform.
Scope of Work
Requirements leadership and executive dashboard delivery
Business Challenge
Detailed requirements spanned multiple business units. Delivery required coordinated business analysis, development, QA, rollout, and managed services across a complex stakeholder landscape.
Our Approach
Led business analysts through structured requirements gathering; developed Tableau and SAP Business Objects dashboards; and managed cross-functional delivery from design through production rollout.
Built a network and subscriber analytics platform enabling teams to probe, monitor, and troubleshoot service quality across the subscriber base.
Scope of Work
Network analytics architecture and data governance
Business Challenge
High-volume network data in Vertica and Teradata required a new analytics domain with consistent data governance, quality standards, and integration patterns.
Our Approach
Delivered a subscriber-analysis toolset with Erwin-modelled data architecture, Oracle Data Integrator integration, and a governance and data-quality framework for the network analytics domain.
Technology Stack
HP VerticaTeradataErwinOracle Data IntegratorZhilabs-CEA
Optimized batch ETL windows and storage costs on Teradata, enabling near-real-time reporting and measurable infrastructure savings.
Scope of Work
Performance tuning, cost optimization, and reporting modernization
Business Challenge
Extended mini-batch execution times and unchecked storage growth on Teradata limited reporting freshness and increased platform operating costs.
Our Approach
Optimized mini-batch execution and ETL scheduling; aligned operations with governance and master data management standards; and delivered Business Objects and QlikView applications for refreshed reporting.
Technology Stack
TeradataBusiness ObjectsQlikViewHadoopHiveOracle Data Integrator
Led BI and data warehouse operations for a national telecommunications provider, maintaining service levels while driving uplift projects and cost optimization.
Scope of Work
Platform operations leadership and continuous improvement
Business Challenge
The organization required sustained platform availability and data-delivery SLAs alongside concurrent uplift initiatives and management of significant OPEX and CAPEX portfolios.
Our Approach
Directed BI uplift programs and day-to-day operations; maintained data dictionary, catalog, and MDM documentation aligned with ITIL practices; and led cost-saving initiatives across the analytics estate.
Led enterprise data management implementation on Teradata for a major telecommunications client, transitioning delivery methodology from Waterfall to Agile mid-program.
Scope of Work
Technical leadership and enterprise data model delivery
Business Challenge
A large distributed team required disciplined release management across SIT, UAT, regression, and production environments while maintaining data quality and architectural consistency.
Our Approach
Led development, release, and configuration management; delivered Erwin entity-relationship models for revenue assurance and customer lifecycle domains; and executed Oracle Data Integrator migration with comprehensive technical documentation.
Designed and delivered a new OLAP semantic layer over the enterprise Teradata warehouse, enabling consistent KPI reporting across business units.
Scope of Work
Data modelling leadership and KPI reporting architecture
Business Challenge
The organization lacked a unified semantic layer over its enterprise warehouse, making cross-functional KPI reporting inconsistent and difficult to scale.
Our Approach
Led conceptual and logical data modelling; delivered Teradata architecture, data models, and reporting design; and built ETL pipelines using SSIS and PL/SQL.
Resolved subject-area data quality issues and implemented ongoing monitoring to restore confidence in enterprise reporting and analytics.
Scope of Work
Data quality program management and monitoring framework
Business Challenge
Data quality issues across multiple subject areas undermined reporting trust. The organization needed proactive monitoring while root causes were systematically addressed.
Our Approach
Implemented a data governance and quality monitoring framework; designed BI and ETL architecture using SSIS and Informatica; and established subject-area quality metrics with escalation workflows.
Built a revenue assurance data platform with operational dashboards and production support, enabling marketing and finance teams to monitor revenue integrity.
Scope of Work
DWH engineering and revenue assurance analytics
Business Challenge
Production revenue assurance workloads required continuous service improvement alongside stable DWH and BI operations under demanding availability expectations.
Our Approach
Developed the DWH and revenue assurance appliance with L1 and L2 production support; delivered Business Objects dashboards for Revenue Assurance and Marketing teams.
