A. Akhila
Data Engineer • Princeton, NJ, USA • a**************@gmail.com • 909****146 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Data Engineer with 5+ years of experience managing the data lifecycle from ETL to data governance in SQL Server on-premises and Azure cloud environments. Experienced building scalable ingestion and transformation pipelines for clinical, financial, and platform telemetry datasets using Azure Data Factory, Azure Databricks, Azure Synapse, Snowflake and Apache Spark. Proven strengths in T-SQL, PySpark, data quality and HIPAA-compliant data handling, delivering analytic datasets for research and regulatory reporting while improving performance, observability, and cost efficiency.
Technical Skills
Programming Languages: Python,Scala,Bash,Java
Databases: SQL,Snowflake,SQL Server,PostgreSQL,MySQL,Oracle,MongoDB
Cloud and DevOps: Azure,Azure Data Factory,Azure Synapse Analytics,Azure Databricks,ADLS Gen2,Azure Event Hubs,AWS,GCP,Terraform,Docker,Kubernetes,CI,CD,Azure DevOps,GitHub Actions,Datadog,Azure Monitor
Data and Analytics: Delta Lake,Amazon Redshift,BigQuery,Lakehouse architecture,Apache Spark,Structured Streaming,Apache Kafka,Apache Flink,AWS Kinesis,Data Quality,Data Observability,HIPAA,GDPR,RBAC,Data Masking,Power BI,Tableau,Looker,DAX
Tools and Methodologies: Git
Skills: PySpark,T-SQL
Orchestration & ETL: dbt,Apache Airflow,Cloud Composer
ML & MLOps: Feature engineering,ML pipelines,AWS SageMaker
Work Experience
Access Healthcare
Princeton, New Jersey, USA
Senior Data Engineer
Jun 2025 – Current
Worked on clinical and health-research data engineering; supported analytical datasets and HIPAA-compliant data delivery for Principal Investigators and research teams.
Tech Stack: Python, PySpark, SQL Server, T-SQL, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, ADLS Gen2, Snowflake, Microsoft Fabric, Power BI, Great Expectations, Terraform, Azure Monitor, GitHub Actions
- Designed and operated ETL ingestion and governance pipelines across SQL Server on-prem and Azure, ingesting CSV, Parquet, JSON and fixed-width clinical files to support research analytics, processing 5+ TB of clinical data daily using ADF, Databricks and Synapse.
- Authored advanced T-SQL queries, views and stored procedures; monitored and tuned SQL Server execution plans to improve performance for multi-table clinical aggregations and analytic dataset creation.
- Built and maintained Azure Data Factory and Microsoft Fabric workflows to standardize ingestion and transformation patterns, reducing pipeline failures and ensuring timely delivery of research-ready datasets to investigators.
- Applied PySpark on Azure Databricks with partition pruning and execution-plan optimization to accelerate large-scale clinical transformations and improve job throughput across high-volume workloads.
- Served as Honest Broker for sensitive clinical data: implemented RBAC, data masking, audit logging and documented HIPAA-compliant procedures to ensure secure access and audit readiness for research datasets.
- Created Power BI semantic models and reporting datasets layered on Synapse and Snowflake; documented ETL specs, data flow diagrams and dataset definitions to support reproducibility and compliance audits.
California Business Bank
Cherry Hill, California, USA
Senior Data Engineer
Jun 2024 – May 2025
Supported banking data pipelines for core banking, credit risk and regulatory reporting; delivered data products used by compliance and executive teams.
Tech Stack: Python, PySpark, Azure Data Factory, Azure Event Hubs, Azure Databricks, Azure Synapse Analytics, Snowflake, dbt, Apache Airflow, Power BI, ADLS Gen2, Azure AD, Git
- Profiled upstream core banking, credit risk and customer master systems to identify 30+ schema inconsistencies before pipeline build, preventing downstream reporting failures during month-end regulatory cycles.
- Engineered ADF batch pipelines and Azure Event Hubs streaming ingest from 8+ source systems to standardize ingestion patterns and ensure reliable data availability for risk monitoring and compliance reporting.
- Developed PySpark transformation workflows in Azure Databricks to apply credit risk business rules and portfolio aggregation logic across 8+ business units, producing audit-ready datasets for reporting.
- Authored and maintained dbt models across staging, intermediate and mart layers with automated tests and reusable macros to enforce data quality and accelerate reportable dataset delivery.
- Prepared ML-ready feature datasets for credit risk scoring and churn models, defining feature engineering logic and refresh cadences to support production model training and scoring.
- Implemented cost-monitoring dashboards and optimized Databricks cluster auto-scaling and spot strategies, reducing pipeline compute costs and unbudgeted cloud spend by ~20% while meeting SLAs.
NTT Data
Hyderabad, India
Data Engineer
Feb 2022 – Jul 2023
Delivered data engineering solutions for financial services clients including loan servicing, mortgage and credit bureau systems to support compliance and fraud detection.
Tech Stack: Python, PySpark, AWS S3, Amazon EMR, Amazon Redshift, AWS Kinesis, AWS Lambda, Apache Kafka, Apache Airflow, Docker, AWS EKS, Git
- Mapped field-level business rules and schema change patterns across loan servicing, mortgage and credit bureau systems to inform ingestion architecture and prevent regulatory reporting failures.
- Built AWS-native batch and near-real-time ingestion pipelines using S3, EMR and Kinesis, reducing fraud detection signal availability from 15 minutes to under 2 minutes via streaming and Lambda triggers.
- Engineered ETL/ELT pipelines with PySpark on EMR and Amazon Redshift to apply credit risk rules and compliance calculations, delivering audit-ready datasets consumed by risk and reporting teams.
- Integrated data observability checks across 50+ critical financial pipelines to monitor freshness, volume anomalies and schema drift, enabling early detection of upstream issues prior to regulatory submissions.
- Implemented schema validation, source freshness monitoring and row-level data quality checks to improve dataset reliability and reporting accuracy for compliance stakeholders.
- Developed a Python-based data quality scorecard aggregating pipeline health metrics and delivering automated weekly reports to compliance teams, eliminating manual Excel-based audits.
Google
Hyderabad, India
Data Engineer
Sep 2019 – Jan 2022
Built telemetry and platform analytics pipelines to support internal engineering and product teams for operational monitoring and product analytics.
Tech Stack: Python, PySpark, Google Cloud Storage, BigQuery, Cloud Composer, Apache Spark, Apache Flink, Git
- Developed distributed PySpark pipelines to ingest and transform platform telemetry, system metrics and user behavior datasets to support operational monitoring and product analytics.
- Designed optimized Spark batch workflows with Google Cloud Storage partitioning and aggregation strategies, improving recurring analytics processing efficiency by ~30%.
- Built BigQuery serving layer with partitioning, clustering and materialized views that improved query performance for operational dashboards by ~40%.
- Implemented Cloud Composer-managed Airflow orchestration for DAG scheduling, dependency management and failure recovery, reducing unplanned pipeline downtime by ~25%.
- Experimented with Apache Flink for stateful stream processing, producing a proof-of-concept real-time aggregation pipeline that reduced analytics latency for internal dashboards.
- Collaborated with internal engineering stakeholders to deliver reliable telemetry datasets and documented transformation logic, contributing to cross-team observability and analytics adoption.
Education
Elmhurst University
Masters in Computer Systems Networking & Telecommunications (MCIT) • Chicago • Aug 2023 – May 2025
Certifications
Microsoft Certified: Azure Solutions Architect Expert — Microsoft
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