Rojan Adhikari

1. November 2025 – Present

   Senior Data Engineer

   Duke Energy (Energy and Utilities) · Contract

   Build and operate AWS cloud data infrastructure for an energy and utilities client, provisioning environments with Terraform and Kubernetes while supporting real-time streaming pipelines built on Apache Flink, Kafka, and Debezium.

   • Build and maintain cloud data infrastructure using AWS, Terraform, Kubernetes, Helm, and GitHub Actions.
   • Support streaming-data workloads using Apache Flink, Apache Kafka, Debezium, and Apache Hudi.
   • Develop and improve CI/CD workflows for building, publishing, and deploying containerized applications.
   • Implement monitoring and alerting using Prometheus, Grafana, Grafana Alloy, and Mimir.
   • Investigate incidents involving container deployments, image availability, checkpoint failures, data duplication, permissions, and pipeline performance.
   • Configure AWS services including IAM, Lake Formation, Glue, S3, KMS, SNS, Lambda, EventBridge, Athena, EKS, and Secrets Manager.
   • Automate infrastructure provisioning and environment configuration through Terraform and deployment workflows.
   • Collaborate with application teams, infrastructure engineers, and product teams to troubleshoot platform issues.
   • Participate in production deployments, on-call rotations, root-cause investigations, and platform support.
   • Create documentation and operational runbooks that help teams safely manage cloud resources and data-platform operations.
   • AWS
   • Python
   • Terraform
   • Apache Flink
   • Apache Kafka
   • Debezium
   • Apache Hudi
   • Kubernetes
   • Helm
   • Docker
   • GitHub Actions
   • Grafana
   • Prometheus

2. Oct 2024 – Oct 2025

   Senior Data Engineer

   Mid First Bank (Financial) · Contract

   Led modernization of a banking data platform, migrating legacy SQL Server and Oracle pipelines to an Azure-based lakehouse on Databricks and Delta Lake, with Airflow-orchestrated ingestion and Snowflake dimensional models for risk, lending, and regulatory reporting.

   • Contributed to the modernization of the bank’s data platform by migrating legacy SQL Server and Oracle data pipelines to an Azure-based lakehouse architecture using Azure Data Lake Storage, Databricks, Delta Lake, and Snowflake.
   • Designed batch and incremental ingestion pipelines using Azure Data Factory, PySpark, and SQL to process customers, account, transaction, loan, and operational data from multiple banking systems.
   • Developed bronze, silver, and gold data layers in Delta Lake, applying schema validation, deduplication, data standardization, and business transformations before publishing curated datasets for analytics.
   • Implemented change data capture and incremental-loading patterns using source timestamps, merge operations, and Delta Lake upserts, reducing unnecessary full-table processing and improving data freshness.
   • Built PySpark and SQL transformation jobs in Databricks to cleanse, join, and aggregate high-volume financial datasets used by risk, lending, finance, and regulatory-reporting teams.
   • Developed dimensional data models in Snowflake, including fact and dimension tables with Slowly Changing Dimension Type 2 logic, to support historical reporting and customer and account analytics.
   • Created and maintained Apache Airflow workflows to orchestrate ingestion, transformation, data-quality validation, and downstream publishing processes with retries, dependencies, and failure notifications.
   • Implemented automated data-quality checks for schema conformity, null values, duplicate records, referential integrity, and source-to-target reconciliation, helping identify data issues before they reached reporting layers.
   • Applied banking-data security controls using Azure RBAC, managed identities, Key Vault, Snowflake role-based access, encryption, and least-privilege permissions to protect sensitive customer and financial information.
   • Developed reusable Terraform modules and GitHub Actions workflows to provision cloud resources, validate infrastructure changes, and promote data-pipeline configurations across development, testing, and production environments.
   • Monitored pipeline execution, Spark workloads, data freshness, and job failures using Azure Monitor, Databricks logs, and operational dashboards; investigated production incidents and documented root causes and corrective actions.
   • Partnered with business analysts, data architects, application teams, and governance stakeholders to translate reporting and data requirements into maintainable data models, pipelines, and technical documentation.
   • Participated in Agile ceremonies, design discussions, pull-request reviews, release planning, and production deployments while supporting and mentoring engineers on PySpark, SQL, and pipeline-development practices.
   • Azure
   • Azure Data Factory
   • Azure Data Lake Storage
   • Databricks
   • Delta Lake
   • Snowflake
   • SQL Server
   • PySpark
   • SQL
   • Apache Airflow
   • Terraform
   • GitHub Actions
   • Azure Monitor

3. Sept 2023 – Oct 2024

   Data Engineer

   Elara Caring (Healthcare) · Contract

   Built a HIPAA-compliant healthcare data platform on Azure, developing Databricks and PySpark pipelines that ingest and transform patient, claims, and HL7/FHIR clinical data into governed Delta Lake and Synapse models with Purview-tracked lineage.

   • Contributed to the development of a HIPAA - compliant healthcare data platform using Azure Data Factory, Azure Data Lake Storage, Databricks, Delta Lake, and Azure Synapse Analytics.
   • Built batch and incremental ingestion pipelines to integrate patient, clinical, claims, provider, and operational data from EHR systems, relational databases, APIs, and secure file transfers.
   • Developed PySpark transformation jobs in Azure Databricks to cleanse, standardize, deduplicate, and enrich healthcare data before publishing curated datasets for analytics and reporting.
   • Designed Bronze, Silver, and Gold data layers in Delta Lake, separating raw clinical data from validated and business - ready datasets while supporting schema evolution and historical processing.
   • Parsed and transformed HL7 and FHIR healthcare messages using Python, PySpark, and JSON processing, converting nested clinical records into standardized structures for downstream consumption.
   • Implemented incremental data - processing patterns using watermark columns, change timestamps, and Delta Lake merge operations to improve data freshness and avoid unnecessary full-table reloads.
   • Created dimensional data models in Azure Synapse for patient outcomes, episodes of care, provider performance, and operational reporting, including historical tracking for changing patient and provider attributes.
   • Developed and maintained Azure Data Factory workflows to orchestrate ingestion, Databricks transformations, data - quality validation, and downstream publishing with dependency management, retries, and failure notifications.
   • Implemented data - quality checks for required fields, duplicate patient records, invalid clinical codes, schema changes, referential integrity, and source - to - target reconciliation before data reached reporting systems.
   • Protected sensitive patient information through Azure RBAC, managed identities, Key Vault, encryption, private endpoints, and role - based access controls aligned with HIPAA and least - privilege requirements.
   • Implemented metadata management and data - lineage capabilities using Microsoft Purview, helping teams understand the movement and transformation of sensitive healthcare data across the platform.
   • Monitored pipeline execution, data freshness, Databricks workloads, and processing failures using Azure Monitor, Log Analytics, and operational dashboards; investigated incidents and documented root causes.
   • Used Terraform and CI / CD workflows to manage cloud resources and deploy notebooks, pipeline configurations, and infrastructure changes consistently across development, testing, and production environments.
   • Collaborated with clinical analysts, application teams, data architects, security teams, and business stakeholders to translate healthcare reporting requirements into scalable pipelines and trusted datasets.
   • Azure
   • Azure Data Factory
   • Azure Data Lake Storage
   • Databricks
   • Delta Lake
   • Azure Synapse Analytics
   • PySpark
   • Python
   • Microsoft Purview
   • Azure Monitor
   • Terraform

4. Feb 2021 – July 2023

   Data Engineer

   Humana (Healthcare Insurance) · Contract

   Built an AWS-based healthcare data platform for claims, enrollment, and provider data, using Glue and EMR for PySpark transformations, Airflow-orchestrated ingestion, and Redshift dimensional models with SCD Type 2, while enforcing HIPAA-aligned access controls.

   • Contributed to the development of an AWS - based healthcare data platform supporting claims, member enrollment, provider, authorization, and clinical reporting workloads.
   • Built batch ingestion pipelines using AWS Glue, Python, and SQL to process data from relational databases, partner file feeds, APIs, and application-generated files into Amazon S3.
   • Developed PySpark transformation jobs using AWS Glue and EMR to cleanse, standardize, deduplicate, and enrich healthcare data before publishing curated Parquet datasets for downstream analytics.
   • Organized datasets into raw, validated, and curated S3 layers with consistent partitioning, naming conventions, and retention policies, improving maintainability and query efficiency.
   • Implemented incremental data - loading patterns using processing timestamps, source-system keys, and change indicators to reduce repeated processing of historical claims and enrollment data.
   • Designed fact and dimension tables in Amazon Redshift for claims, member enrollment, providers, and authorizations, including Slowly Changing Dimension Type 2 logic for historical attribute tracking.
   • Developed and maintained Apache Airflow workflows to orchestrate ingestion, transformation, data - quality validation, and Redshift loading processes with dependencies, retries, and operational notifications.
   • Implemented data - quality checks for duplicate claims, missing member identifiers, invalid dates, code - format violations, referential integrity, and source - to - target record reconciliation.
   • Optimized S3 and Redshift workloads through partition pruning, appropriate file sizing, compression, distribution styles, and sort keys to improve recurring analytics and reporting performance.
   • Applied security controls using AWS IAM roles, KMS encryption, S3 bucket policies, Secrets Manager, and role-based Redshift access to protect member and claims information in accordance with HIPAA requirements.
   • Created CloudWatch logs, metrics, and alerts for failed jobs, delayed data, and processing errors; investigated production issues and documented remediation steps for recurring failures.
   • Used Terraform and CI / CD pipelines to provision AWS resources and deploy Glue jobs, Airflow configurations, and supporting infrastructure consistently across environments.
   • Developed curated datasets and SQL views used by Tableau and Power BI dashboards for claims utilization, member enrollment, provider performance, and operational reporting.
   • Collaborated with analysts, application teams, healthcare - domain specialists, and senior engineers to clarify source - system behavior, validate business rules, and deliver reliable datasets for reporting.
   • AWS
   • AWS Glue
   • EMR
   • S3
   • Redshift
   • PySpark
   • Python
   • SQL
   • Apache Airflow
   • Terraform
   • CloudWatch
   • Tableau
   • Power BI

I’m interested in opportunities involving data engineering, cloud infrastructure, streaming systems, and platform engineering. Feel free to reach out if you would like to discuss a role, project, or technical collaboration.

LinkedIn

www.linkedin.com/in/rojan998 (opens in a new tab)

GitHub

github.com/Rojan998 (opens in a new tab)

Location

Dallas, Texas