Vinay Bommireddy

+•• ••••• •••••••••••@•••••.com

Data Engineer · Signify Health (a CVS Health company) · 4.3 yrs

As a Data Engineer, I am driven by a commitment to logic and mastery, ensuring the development of robust and reliable data solutions. My diligent and accountable approach means I consistently deliver high-quality, pragmatic outcomes, always striving for precision and efficiency in my work.

  • Hyderabad
  • Open to Hyderabad · Bangalore
  • Hybrid

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Skills

Claimed

Problem SolvingStakeholder Communication AdaptationRoot Cause AnalysisFailure Mode AnalysisBusiness AnalysisData Quantity ManagementSource System IdentificationAdaptabilityWork Planning and PrioritizationData Integrity ManagementData GovernanceAI Solution Implementation

Proof of Experience

Data Engineer

Signify Health (a CVS Health company) · Jul 2024 – Present

  • Sustained 99.5% uptime for near-real-time patient and operations visibility by architecting a multi-terabyte ELT pipeline in Azure Data Factory, Databricks, and Snowflake that curates clinical and claims data from Azure Data Lake into Synapse.
  • Reduced validation errors and incidents by 40% by building a dbt transformation layer with automated reconciliation, referential-integrity, uniqueness, and dbt-expectations tests across Snowflake and Synapse models.
  • Cut PySpark job latency by 25% on multi-terabyte healthcare datasets, while preserving accuracy and secure, HIPAA-aligned data governance, by applying partition pruning, broadcast joins, and adaptive query execution on Databricks.
  • Lowered compute costs by 35% and enabled sub-15-minute refresh cycles for clinical dashboards by replacing full-table reloads with schema-evolving incremental ingestion using Delta Live Tables and Databricks Auto Loader.

Data Engineer

State Street Corporation · Jul 2022 – Jul 2024

  • Cut batch processing time by 35% and enabled near-real-time reporting for operations teams by engineering ETL pipelines in Azure Data Factory, Databricks, and Informatica for large-scale financial datasets.
  • Reduced manual intervention by 50% and improved SLA adherence by orchestrating batch and streaming workflows across distributed financial systems with Azure Functions, Logic Apps, and Airflow DAGs.
  • Reduced pipeline failure incidents by 30% across enterprise financial workloads by designing fault-tolerant Airflow orchestration (retries, dependency chaining, alerting) for multi-stage ADF and Databricks jobs.
  • Cut anomaly-detection time from hours to under 10 minutes and enabled intraday position visibility by building real-time CDC (Kafka Connect) and Spark Structured Streaming pipelines that stream trade updates from SQL Server to the cloud data lake.

Proof-of-Work

No completed sessions yet. Anything Vinay finishes shows up here with the evidence behind it.

Proof of Education

M.S., Data Science & Analytics

Grand Valley State University · 2020 — 2022

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