Avinash Alladishetty

Senior GCP Data Engineer and Data Testing Specialist with 10+ years of progressive IT experience, including 7+ years architecting and delivering enterprise-grade data pipelines and 4+ years implementing comprehensive GCP-native data quality and testing frameworks. Proven expert in the full spectrum of GCP services — BigQuery, Cloud Dataflow, Cloud Dataproc, Cloud Storage (GCS), Pub/Sub, Cloud Functions, Cloud Run, Cloud Composer, Vertex AI, and Dataplex — with a consistent track record of ensuring data integrity, reliability, and accuracy across multi-terabyte production environments. Recognized as a go-to leader for designing and implementing automated data testing strategies that eliminate defects before they reach downstream consumers, and for building reusable ingestion, transformation, and validation frameworks that standardize and accelerate delivery across engineering teams.

Proficient with industry-leading data quality tools including Great Expectations, dbt testing frameworks, BigQuery SQL assertions, Dataplex quality scans, and custom Python/SQL validation frameworks. Skilled at building testing pipelines that perform schema validation, null-check enforcement, row-count reconciliation, referential integrity checks, SLA breach detection, and anomaly identification — all integrated into CI/CD workflows for continuous, automated quality assurance. Deep expertise in ETL/ELT pipeline design and modernization — migrating legacy Teradata (BTEQ, TPT, FastLoad/MultiLoad), SSIS, and Informatica PowerCenter workloads to cloud-native BigQuery, Dataflow, and Dataproc architectures using Google's BigQuery Migration Service and Database Migration Service to accelerate on-prem-to-GCP conversions.

Incorporates CDC frameworks, metadata-driven ingestion, and Delta Lake medallion architecture (Bronze/Silver/Gold) with quality gates embedded at every tier. Extensive experience designing star/snowflake schemas, SCD Type 1/2/3 logic, Data Vault models, and BigQuery enterprise warehouse solutions with partitioning, clustering, and materialized views that consistently deliver 40–45% cost reductions and performance improvements.

Proficient

Python and SQL developer writing clean, maintainable, test-driven code for pipeline automation, FastAPI microservices, REST APIs, and LLM-powered tooling integrated with Vertex AI. Well-versed in containerized deployments via Docker and Kubernetes/GKE, CI/CD automation using GitHub Actions, and observability via Cloud Logging, Dynatrace, and Cloud Monitoring. Healthcare domain expert from Humana, with firsthand experience governing patient and claims data under strict SLA requirements and HIPAA-aware compliance standards.

Collaborative mentor who champions Agile delivery, test-driven development, and documentation best practices across engineering teams.

Senior Data Engineer - CDW - Chicago, IL

(2022-03)

Led modernization of a large-scale enterprise analytics platform on GCP and Azure. Established automated data testing frameworks, governed BigQuery data products with embedded quality gates, and delivered reliable audit-ready data pipelines serving 15+ business domains.

• Designed and deployed end-to-end data quality frameworks using Great Expectations, BigQuery SQL assertions, and Cloud Monitoring alerts — enforcing row-count reconciliation, null checks, referential integrity, and schema validation across 20+ production datasets, reducing data defects reaching downstream consumers by 40%.
• Built Python-based automated test suites integrated into GitHub Actions CI/CD pipelines to validate data pipelines pre-deployment — covering schema drift detection, transformation accuracy, SLA threshold checks, and anomaly detection for all BigQuery load jobs.
• Implemented GCP Dataplex quality rules and continuous scanning schedules on GCS and BigQuery assets, enabling data profiling, freshness monitoring, and lineage-aware quality scoring across the enterprise data lake.
• Built and maintained PySpark jobs on Cloud Dataproc alongside Cloud Dataflow streaming and batch pipelines supporting schema evolution, SCD Type 1/2, surrogate key generation, and multi-terabyte transformations — deployed on GKE and Cloud Run with automated test gates at each stage.
• Architected enterprise BigQuery solutions with partitioning, clustering, and materialized views — reducing query costs by 45% and improving SLA compliance for high-frequency analytical workloads across 15+ business domains.
• Designed Bronze/Silver/Gold Delta Lake medallion layers (BigQuery + GCS + Azure Databricks) with embedded data quality checkpoints at each tier — enabling governance, lineage tracking, and a 40% reduction in downstream rework.
• Migrated legacy SSIS packages to Cloud Dataflow and ADF pipelines with robust error handling, Cloud Logging, and automated alerting frameworks — cutting maintenance effort by 35% and improving production reliability.
• Designed event-driven GCP Pub/Sub + Cloud Functions + Dataflow pipeline processing 10M+ daily events with sub-second latency and integrated quality checks before BigQuery landing.
• Designed parameter-driven metadata ingestion templates on GCP supporting 20+ datasets with automated schema drift detection, self-healing transformation logic, and GCS-based centralized staging — built as reusable frameworks adopted across multiple engineering pods.
• Orchestrated multi-stage ingestion and transformation workflows with Cloud Composer (managed Airflow), standardizing DAG patterns and retry/alerting logic as a reusable accelerator for new pipeline onboarding.
• Implemented BigQuery ML models for churn prediction and anomaly detection; integrated GCP Data Catalog and Looker for self-service data discovery enabling query exploration across 15+ domains.
• Integrated Dynatrace and Cloud Monitoring for API performance observability, pipeline health dashboards, and SLA tracking — enabling proactive issue detection and reducing mean-time-to-resolution.
• Mentored junior engineers on GCP data testing practices, test-driven development, and quality-first engineering standards; authored internal data quality playbooks and pipeline governance runbooks adopted org-wide.

Cloud Data Engineer - CenterPoint Energy - Houston, TX

(2021-11 - 2022-03)

Contributed to a major enterprise ETL modernization initiative — migrating Informatica and Teradata workloads to GCP/IICS, implementing CDC frameworks, and building data quality validation routines across GCP, Azure, and AWS multi-cloud environments.

• Analyzed and migrated legacy Teradata BTEQ scripts and stored procedures to BigQuery, using TPT and FastLoad/MultiLoad exports to stage historical data in GCS ahead of load — accelerated with Google's BigQuery Migration Service and Database Migration Service to assess and convert on-prem workloads.
• Built Python and SQL-based data quality routines with custom BigQuery validation scripts — cleansing, validating, and enriching enterprise datasets to meet regulatory compliance and business SLA requirements.
• Implemented and validated Change Data Capture (CDC) frameworks across SQL Server, Oracle, PostgreSQL, and DB2 — streaming verified changes to BigQuery via Pub/Sub and Dataflow with automated completeness, consistency, and duplicate-check validation.
• Migrated Informatica PowerCenter workloads to IICS with built-in quality checkpoints at ingestion, transformation, and load stages — improving scalability and boosting pipeline throughput by 30%.
• Designed and tested Cloud Dataflow, ADF, and AWS Glue pipelines integrating SQL Server, Oracle, DB2, S3, and GCS sources into BigQuery with schema validation and row-count reconciliation at each ingestion stage.
• Built and validated GCP Pub/Sub, Azure Service Bus, and AWS SNS/SQS integrations — verifying message delivery guarantees, deduplication logic, ordering semantics, and downstream BigQuery data consistency.
• Governed and tested data APIs using GCP Apigee and Azure APIM — enforcing rate-limiting, authentication policies, and data contract standards.