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OPEN TO SENIOR DATA ENGINEERING ROLES

SENIOR DATA ENGINEER · RALEIGH, NC

I build cloud data platforms for batch and streaming workloads.

I’m Bhanu Vejendla, a Senior Data Engineer with 7+ years of experience turning high-volume data into reliable systems across Azure, AWS and GCP.

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Raleigh, NC · Azure, AWS & GCP
Review my professional experience ↓

platform_overview.yaml LIVE DATA FLOW
INGESTION BATCH + STREAM QUALITY CONTRACTS + TESTS OPERATIONS MONITORED + RECOVERABLE
PIPELINE TELEMETRY
INGESTING EVENTS
SOURCES READY
API / CDC
EVENTS / FILES
INGEST READY
KAFKA
AIRFLOW / ADF
PROCESS READY
SPARK
DATABRICKS
STORE READY
DELTA LAKE
SNOWFLAKE
SERVE READY
BI / ML
DATA PRODUCTS
schema contracts quality gates lineage & observability failure recovery
7+ years engineering
data platforms
3 cloud ecosystems
Azure · AWS · GCP
5 featured engineering
projects
4 industry domains
Insurance · Gaming · Financial Services · Healthcare
01 · ABOUT

I care about what happens after a pipeline goes live.

A good data platform should be reliable today and understandable six months from now.

My work has taken me across insurance, gaming, financial services and healthcare. I’ve built pipelines that ingest, process and organize data for analytics, reporting and downstream applications.

I focus on the work that keeps a platform useful after launch: data quality, failure recovery, monitoring, security, performance and cost. I also build reusable patterns so other engineers can deliver and support pipelines more consistently.

02 · EXPERIENCE

What I’ve worked on.

Each role has strengthened a different part of my data engineering experience.

METLIFE · CARY, NORTH CAROLINA

Azure Data Engineer

At MetLife, I build and maintain Azure pipelines with Kafka, NiFi, Data Factory and Databricks. I’ve also developed reusable Python patterns for Synapse integration and change data capture.

EPIC GAMES · CARY, NORTH CAROLINA

AWS Data Engineer

At Epic Games, I developed AWS workflows with Spark, Glue, Snowflake and Redshift. Automating infrastructure with Terraform reduced management time by 90% and helped improve uptime.

INCRED FINANCIAL SERVICES · MAHARASHTRA, INDIA

GCP Data Engineer

At InCred, I built GCP pipelines with Dataflow, Apache Beam, Dataproc, BigQuery and Spark, and monitored jobs for performance and cost.

SIEMENS HEALTHINEERS · MAHARASHTRA, INDIA

Data Engineer

At Siemens Healthineers, I developed batch and streaming ETL pipelines with Kafka, Spark Streaming, Python and Snowflake, supported by validation and data-quality checks.

03 · TECHNICAL STACK

Tools I’ve used to deliver data systems.

Grouped by where they fit in the work, from ingestion through production delivery.

01
INGESTION & ORCHESTRATION

Reliable movement from source to platform.

Kafka Airflow Azure Data Factory APIs & Postman Apache NiFi AWS Glue Cloud Dataflow Apache Beam Java Message Service
02
PROCESSING & MODELING

Batch and streaming transformations at scale.

Apache Spark Databricks Python & PySpark dbt SQL Hadoop Apache Storm Dataproc Java
03
LAKEHOUSE & WAREHOUSE

Governed storage and analytics-ready models.

Snowflake ADLS Amazon S3 BigQuery Delta Lake Apache Parquet PostgreSQL Azure Blob Storage Azure Synapse Azure SQL Warehouse Amazon Redshift DynamoDB Elasticsearch SnowSQL
04
CLOUD, PLATFORM & DELIVERY

Repeatable infrastructure and production delivery.

Microsoft Azure AWS Google Cloud Terraform Docker Kubernetes GitHub Actions Jenkins Git Grafana Kibana AWS Lambda Cloud Monitoring Looker Studio Docker Swarm
ENGINEERING PRINCIPLES

Idempotent pipelines · Data contracts · Automated testing · Checkpointing · Backfills · Lineage · Monitoring · Secure access

04 · ENGINEERING WORKFLOW

How I take a pipeline from idea to reliable operation.

The tools change by platform. The engineering discipline stays consistent.

delivery_workflow.yaml DISCOVERING SOURCES
PIPELINE STATE HEALTHY QUALITY GATE PASSING RECOVERY IDEMPOTENT
  1. 01 · DISCOVER

    Understand the source

    Volume, latency, ownership and failure modes.

  2. 02 · CONTRACT

    Define expectations

    Schemas, quality rules, SLAs and access.

  3. 03 · INGEST

    Move data safely

    Replayable batch or streaming ingestion.

  4. 04 · TRANSFORM

    Build trusted models

    Tested, incremental and scalable processing.

  5. 05 · DELIVER

    Serve the right shape

    Governed tables and analytics-ready products.

  6. 06 · OPERATE

    Design for reality

    Monitoring, recovery, performance and cost.

✓ Schema contracts ✓ Quality checks ✓ Lineage ✓ Observability ✓ Failure recovery
05 · SELECTED ENGINEERING WORK

Projects that show how I work.

Five projects showing production ownership, practical architecture and reliability—from a live community platform to data engineering systems.

Choose a project to review the problem, engineering decisions and supporting evidence. Each case study links to the live site or source repository.

LET’S TALK · SENIOR DATA ENGINEERING

Let’s talk about your next data engineering hire.

Building a team around batch pipelines, streaming systems or cloud data platforms? Share the role, location and the engineering challenges you’re working on.

Email me about a role Connect on LinkedIn

Prefer to copy my email? bhanumanideepvejendla@gmail.com