Tejas Dalbhanjan
Contact details — on requestProof of Work — on request
Experience
Data Engineer
IBM · Feb 2026 – Present
- Data Engineer with ~5 years of experience building and optimizing enterprise data platforms in cloud environments.
- Responsible for designing and implementing scalable data pipelines, developing distributed data processing workflows, and supporting analytics and reporting use cases.
- Contributing to data architecture improvements, performance optimization, and data quality initiatives within large-scale enterprise programs.
Data Engineer
IBM · Mar 2024 – Present
- Designed and implemented an Azure Lakehouse platform processing over 2 TB of SAP data daily across 80+ production pipelines, enabling standardized ingestion, historical data management, and enterprise analytics using the Medallion Architecture.
- Architected reusable metadata-driven ETL/ELT frameworks supporting 80+ production pipelines and 50+ SAP tables, reducing development effort and accelerating onboarding of new data sources.
- Built reusable ingestion frameworks for 50+ SAP tables, reducing manual onboarding effort by 70% while improving maintainability and operational reliability.
- Optimized Spark workloads processing multi-terabyte production datasets through partitioning, Delta Lake optimization, predicate pushdown, caching, and file compaction, reducing query execution time by 3x and storage costs by 20%.
- Designed dimensional data models using fact and dimension tables with SCD Type 1 and Type 2 implementations, enabling historical reporting and business analytics.
- Developed automated data quality frameworks incorporating schema validation, source-target reconciliation, duplicate detection, completeness checks, and exception handling, significantly improving trust in downstream analytical datasets.
- Published curated Gold-layer datasets through Azure Synapse Serverless SQL, enabling Power BI reporting and self-service analytics for business users.
- Owned production support for Azure data pipelines by leveraging Azure Monitor for proactive monitoring, incident resolution, root cause analysis (RCA), Spark performance tuning, and SLA compliance for business-critical workloads.
- Integrated Azure Key Vault to securely manage secrets and service credentials across data pipelines.
- Improved reporting performance by 25% through SQL query optimization, efficient joins, CTE restructuring, window functions, and data model refinements.
- Implemented CI/CD pipelines using Azure DevOps and Databricks Workflows, reducing deployment effort and improving release consistency across environments.
- Collaborated with architects, product owners, analysts, and business stakeholders to translate business requirements into scalable Azure data solutions while mentoring junior engineers and driving engineering best practices.
Data Engineer
Neudesic, an IBM Company · Mar 2024 – Jan 2026
- Acquired by IBM in January 2026.
- Continued in the same role under IBM.
Data Engineer
Bitwise India · Apr 2022 – Feb 2024
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Data Engineer
Bitwise Solutions · Jul 2021 – Feb 2024
- Developed and maintained Azure Databricks ETL pipelines processing financial transaction datasets for downstream reporting, reconciliation, and analytics.
- Designed batch, incremental, and CDC-based ingestion pipelines using Azure Data Factory, PySpark, SQL, and Delta Lake to automate data movement from multiple enterprise source systems.
- Built optimized enterprise data warehouse solutions using Parquet, ORC, partitioning strategies, and efficient storage formats to improve analytical query performance.
- Improved ETL pipeline execution time by 31% through Spark optimization, partition tuning, workflow automation, and resource utilization improvements.
- Enhanced reporting performance by optimizing complex SQL queries, indexing strategies, joins, and execution plans, reducing report latency by up to 20%.
- Implemented dimensional models using SCD Type 1 and Type 2 to support historical analysis, regulatory reporting, and business intelligence initiatives.
- Performed production monitoring, root cause analysis, and proactive issue resolution to reduce recurring failures and improve data pipeline reliability.
- Partnered with analytics and reporting teams to deliver trusted datasets supporting operational reporting, KPI dashboards, and business decision-making.
- Participated in Agile development, code reviews, Azure DevOps CI/CD implementation, Apache Airflow scheduling, Git version control, and Databricks Workflows orchestration.
Trainee programmer
Bitwise India · Oct 2021 – Mar 2022
Project Trainee
Bitwise India · Jul 2021 – Oct 2021
Skills 0 proven through work
Also works with
Stakeholder ManagementTeam CommunicationProcess ImprovementContextual AnalysisBlocker Identification and ResolutionProject Status CommunicationTaking OwnershipReliabilityTeamworkProblem SolvingAdaptabilityDependency ManagementData CollectionWork Planning and PrioritizationSenior Engineering LeadershipCost-Saving Initiative ManagementGuidanceReliability ImprovementScalable System DesignData GovernanceQuery WritingPython DevelopmentSoftware Development PracticesData Architecture DesignRequirements AnalysisClient FocusTeam LeadershipMentoring and Peer CoachingCloud Architecture AdvisoryAzure Synapse Analytics UsagePerformance TestingBroadcast Join ImplementationEfficient SQL Data SelectionDatabase Query OptimizationLog AnalysisData Partitioning and Segregation DesignData Discrepancy InvestigationData Unification and NormalizationBusiness Transaction AnalysisBusiness AcumenTechnical TroubleshootingAudit Log ImplementationStructured LoggingMonitoring AutomationError HandlingBusiness Rules VerificationTransaction ReconciliationQuality Audit Framework DesignData LoadingFramework DevelopmentData Pipeline ParameterizationEnvironment-Specific Configuration ManagementSAP IntegrationApplication MonitoringData OptimizationDatabase Query Caching ImplementationPartition Pruning ImplementationApplication Performance OptimizationData ValidationSlowly Changing Dimensions ImplementationData Repetition PreventionTabular Data Cleaning & ValidationIncremental Data ProcessingData TransformationMetadata-Driven ETLMedallion Architecture DesignPySparkDatabricks UsageADLS Gen2 UsageAzure Data Factory Pipeline DevelopmentETL Pipeline DevelopmentAzure Data Lake UsageData Engineering
Proof of Work
Proof of Work
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Education
Bachelor of Engineering, Engineering
Savitribai Phule Pune University · 2017 — 2021
Contact details
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