Data Engineer A Team

Kishore Sakamuri

Email: *******.*.****@*****.***

Mobile: +1-978-***-****

Data Engineer

PROFESSIONAL SUMMARY:

Data Engineer with 8+ years of experience, demonstrating strong analytical thinking and innovative problem-solving skills across diverse data workflows, ensuring attention to detail. A team player who can influence and guide team for success.

Proficient in connecting dots across applications to understand E2E view, with strong PL/SQL skills for complex queries and stored procedures, working well in a team environment with minimal supervision.

Experienced in Oracle Exadata or 10g and above, supporting data analysis and reporting, with expertise in Microsoft Office suite usage and ability to write and analyze complex queries.

Skilled in developing transformation logic and preparing business-ready datasets, identifying priorities, and managing multiple projects simultaneously, demonstrating strong communication and presentation skills.

Implemented CI/CD pipelines using Azure DevOps, Git, and Terraform, ensuring seamless deployments, version control, and infrastructure automation for cloud-based data engineering environments.

Built real-time ingestion pipelines using Azure Event Hub, Kafka, and Spark Structured Streaming to support operational dashboards, alerts, and real-time data processing for critical workflows.

Developed ETL workflows in GCP using Cloud Dataflow (Apache Beam) and Pub/Sub for streaming and batch pipelines, improving latency and throughput for high-volume data ingestion.

Integrated BigQuery and Looker to enable self-service BI across teams, creating reusable semantic layers, governed datasets, and optimizing analytical performance using clustering and partitioning.

Migrated legacy SSIS and SQL workloads to cloud-native data pipelines, reducing infrastructure complexity, operational failures, and processing time while improving availability and cost-efficiency.

Implemented secure cross-cloud communication using IAM roles, Azure Key Vault, and GCP Secret Manager, ensuring compliance, role-based access, and encrypted credentials in data pipelines.

Designed efficient data warehouses using BigQuery and Synapse, utilizing materialized views, federated queries, and cost-aware schema design for large-scale reporting and dashboarding workloads.

Developed resilient error handling, auto-retry logic, and alerting frameworks in Azure Data Factory and Cloud Composer to improve observability and operational support for data pipelines.

Built automated data lineage and metadata management using Azure Purview and GCP Data Catalog, enhancing governance, auditing, and impact analysis for enterprise data assets.

Engineered ingestion of healthcare APIs and transformed HL7, JSON, and flat files into structured formats for downstream analytics, meeting regulatory compliance and business intelligence needs.

Supported ML pipeline deployment using Azure ML and Vertex AI, preparing curated datasets, feature engineering workflows, and integrating models with batch and streaming data sources.

Experienced Data Engineer with Azure certifications, skilled in ETL, cloud solutions, and BI, supporting data-driven decisions across healthcare, finance, and technology sectors.

TECHNICAL SKILLS:

Languages & Frameworks - SQL (T-SQL, BigQuery SQL), Python, PySpark, Apache Beam, PL/SQL

Databases & Warehousing - Azure SQL, BigQuery, Azure Synapse, PostgreSQL, SQL Server, Oracle Exadata, Oracle 10g

DevOps Tools - Git, Azure DevOps, Terraform, CI/CD Pipelines, Cloud Build

Visualization Tools - Power BI, Looker

Other Tools - Azure Key Vault, GCP Secret Manager, Data Catalog, Azure Purview, Microsoft Office Suite

Processes - Agile, Scrum

PROFESSIONAL EXPERIENCE:

Blue Cross Blue Shield (BCBS) January 2025 – Present

Senior Data Engineer

Responsibilities:

Demonstrated analytical thinking and problem-solving skills by designing and deploying scalable ETL/ELT pipelines using Azure Data Factory and Databricks, optimizing performance across enterprise domains. This involved innovative thinking to integrate clinical and insurance data effectively.

Orchestrated real-time data ingestion using Event Hub, ADLS Gen2, and ADF pipelines, enabling rapid processing of member eligibility and claims datasets for downstream analytical consumption, showcasing attention to detail. Strong communication skills were essential.

Built Spark-based transformation logic in Databricks and wrote outputs to Synapse dedicated pools, optimizing OLAP performance and reducing query latency for analytics teams, requiring strong PL/SQL skills. This improved the E2E view.

Automated release management using CI/CD pipelines with Azure DevOps and ARM templates, reducing deployment failures and enabling standardized, version-controlled pipeline deployments across environments, demonstrating innovative thinking.

Integrated Azure Purview with data pipelines for end-to-end lineage tracking, enabling governance, impact analysis, and regulatory audit readiness across sensitive datasets and transformations, requiring attention to detail.

Engineered Delta Lake ingestion architecture for capturing change data across historical loads, ensuring full transactional integrity and traceability across patient and claims records, showcasing analytical thinking and problem-solving skills.

Delivered executive dashboards in Power BI using curated datasets from Synapse, surfacing KPIs and real-time metrics for business decision-making in clinical and operations domains, requiring strong communication and presentation skills.

Implemented HIPAA-compliant data security using Key Vault, private endpoints, VNET integration, and RBAC, ensuring encryption and access control across all data access and movement, demonstrating attention to detail and innovative thinking.

Integrated Azure Monitor with pipeline activities to proactively detect, alert, and remediate failed data loads, enabling continuous uptime and adherence to SLAs, showcasing analytical thinking and problem-solving skills.

Refactored pipeline logic by replacing costly lookups with partitioned joins and modular flows, improving performance efficiency by 35% and reducing total pipeline runtime significantly, demonstrating innovative thinking and attention to detail.

Pacific Dental Services January 2024 – December 2024

Data Engineer

Responsibilities:

Built scalable Azure data pipelines for appointment analytics and revenue forecasting across 800+ clinics, enabling daily performance tracking and strategic planning for clinical and business operations, demonstrating analytical thinking.

Created reusable ADF templates for standardized ingestion of EHR and operational data into ADLS, enhancing pipeline maintainability and reducing onboarding time for new data sources, requiring attention to detail and innovative thinking.

Implemented Delta ingestion pipelines in Databricks to track changes in appointment schedules, cancellations, and revenue updates, ensuring near real-time accuracy in operational datasets, showcasing problem-solving skills.

Designed Power BI dashboards powered by Synapse models to deliver financial insights and KPIs to the business team, supporting planning, forecasting, and executive decision-making, requiring strong communication skills.

Managed batch ingestion from external vendors using ADF triggers and processed insurance claims from XML files delivered via SFTP into structured datasets for reporting, demonstrating attention to detail and analytical thinking.

Collaborated with solution architects to apply fine-grained RBAC in ADLS and ensured compliance with PHI security requirements through encryption, private endpoints, and audit controls, showcasing problem-solving skills.

Designed a cross-region replication solution for Azure Blob Storage to maintain redundant backups of sensitive patient data and ensure availability during disaster recovery scenarios, demonstrating innovative thinking and attention to detail.

Developed alerting workflows in Azure Logic Apps to notify support teams of SFTP ingestion failures and reduce pipeline downtime through timely intervention and resolution, requiring analytical thinking and problem-solving skills.

Developed a custom audit logging solution using Azure Functions to track ingestion status, log pipeline errors, and deliver complete operational visibility across enterprise data workflows, demonstrating attention to detail.

Reengineered legacy SSIS packages using Azure Data Factory, enabling event-driven ingestion of daily patient records triggered by file drops from external clinic systems, showcasing problem-solving skills and innovative thinking.

BOFA - Accenture April 2019 – July 2022

Data Engineer

Responsibilities:

Developed Spark ingestion pipelines in Databricks to process financial transaction logs, supporting high-throughput data processing and compliance reporting for risk and fraud analytics, demonstrating analytical thinking and problem-solving skills.

Implemented Change Data Capture (CDC) pipelines in Azure Data Factory to extract incremental data from Oracle and SQL Server sources, optimizing loads and reducing total data processing time, requiring attention to detail.

Engineered PySpark transformations to clean, enrich, and standardize structured and semi-structured data such as transaction logs, fraud alerts, and payment records for analytics teams, showcasing innovative thinking.

Built metadata-driven ingestion framework leveraging parameterized notebooks and control tables, enabling dynamic onboarding of new sources and simplifying long-term maintenance of ETL workflows, requiring strong PL/SQL skills.

Developed scalable micro-batch and streaming solutions using Kafka and Spark Structured Streaming to process high-frequency events in near real-time with fault tolerance and checkpointing, demonstrating analytical thinking.

Contributed to the development of Power BI dashboards by preparing and delivering trusted, curated datasets supporting risk analytics, operational insights, and fraud detection reports, requiring strong communication skills.

Implemented schema validation utilities, logging frameworks, and dead-letter queue logic in Spark to isolate corrupt records and ensure resilient data pipelines across environments, demonstrating attention to detail.

Strengthened platform security by enforcing RBAC policies, integrating Azure Key Vault for key rotation, and establishing fine-grained access control across ingestion, transformation, and reporting layers, showcasing problem-solving skills.

Improved reliability by implementing backfill strategies, checkpointing, and retry logic for Spark jobs, minimizing job failures and reducing manual remediation during high-volume data loads, demonstrating analytical thinking.

Automated unit testing using Pytest to validate ETL pipelines in Databricks, increasing code quality and reducing deployment risks across development, staging, and production environments, requiring attention to detail.

British Telecom January 2015 – March 2019

BI Developer

Responsibilities:

Designed and implemented scalable ETL pipelines using SSIS to efficiently handle telecom provisioning, billing, and service datasets across multiple regional data marts for improved performance, demonstrating analytical thinking.

Developed robust SSIS packages for automated nightly ingestion of service records, provisioning updates, and billing metrics into the centralized SQL Server data warehouse environment, requiring attention to detail.

Tuned and maintained stored procedures to calculate key business metrics such as SLA adherence, churn rate, and average revenue per user (ARPU), ensuring real-time business reporting accuracy, showcasing problem-solving skills.

Led migration of high-volume ETL logic into PySpark workflows, enabling seamless transition to a Hadoop-based data lake platform for scalable distributed data processing, demonstrating innovative thinking and analytical thinking.

Created interactive Power BI dashboards using complex DAX expressions and row-level security (RLS) to deliver targeted insights for executives and regional managers, requiring strong communication and presentation skills.

Managed SQL Server Agent job scheduling with custom alerting logic and failure handling strategies, ensuring maximum uptime and minimal manual intervention for ETL jobs, demonstrating attention to detail and problem-solving skills.

Built and deployed automated data validation routines that cross-checked schema integrity, flag rule violations, and performed record count reconciliations post-load, requiring analytical thinking and attention to detail.

Partnered with cross-functional teams to define and develop new business KPIs through the creation and optimization of SSAS tabular models for enterprise-wide analytics, requiring strong communication skills and innovative thinking.

Documented all transformation logic, business rules, and metadata mappings to support audit readiness and streamline onboarding for future data engineering staff, demonstrating attention to detail and analytical thinking.

Achieved 35% reduction in nightly ETL processing time through batch optimization, query tuning, and redesigning join strategies to minimize I/O overhead, showcasing problem-solving skills and innovative thinking.

Certifications:

AZ-204: Microsoft Certified – Developing Solutions for Microsoft Azure

AZ-900: Microsoft Certified – Azure Fundamentals

Educational Details:

Master of Science in Computer Information - Trine University, Angola, Indiana, USA

Bachelor's Degree in Technology - JNT University, Kakinada, India

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