13.03.2026 aktualisiert

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Big Data/Cloud(AWS) Engineer/Architect

Falkensee, Deutschland
Deutschland
Masters in Software Technologie, Universität Lüneburg
Falkensee, Deutschland
Deutschland
Masters in Software Technologie, Universität Lüneburg

Profilanlagen

Resume_vbalaramiah_de.pdf

Über mich

Senior Technical Lead and Data/Software Architect with 20 years of experience designing and delivering large‑scale data platforms, streaming systems, and applied machine learning solutions in automotive, e‑commerce, consulting, and telecom. Expert in Kafka/Spark

Skills

JavaJavaScriptActivesyncAPIsAmazon Web ServicesAmazon S3Data AnalysisBig DataCSSCloud ComputingApache LuceneCOBOLETLData VaultDirect ConnectElasticsearchApache HadoopApache HbaseHypertext Transfer Protocols (HTTP)HibernateApache HiveIbm Websphere MqImap-ServerJava Database ConnectivitySpring FrameworkJSONJavaserver FacesPostgreSQLMachine LearningGroßrechnerMongoDBMySQLNatural Language ProcessingNeo4jOpenShiftOracle DatabasesOracle FinancialsRabbitMQMicrosoft Power BIElastic LogstashPrometheusScalaStatistikenVersionierungTestenGrafanaApache SparkSpring BootElectronic Medical RecordsBackendKubernetesCassandraAvroApache KafkaSuchmaschinenOracle Golden GateGraphQLSpark StreamingApi-GatewayElastic KibanaTerraformSoftware Version ControlDockerMicroservices
Data Ingestion, Scala, Java, Python, Oracle, CDC data, Oracle, Airflow, Spark Streaming, S3, Prometheus, Grafana, Data Vault, analytics, Hbase, Hive, AWS, Terraform, Postgres, DB, AWS Quick Sight, Power BI, JDBC, EMR, Spark, ETL, Cloud, SNS, machine learning, Sage Maker, docker, AWS Direct Connect, Microservices, APIs, API Gateway, throttling, Mainframe, IBM MQ, Kafka, COBOL, Java based application, Openshift, versioning, Version Control, search engine, text search, Elastic, GraphDB, Neo4j, GraphQL, Kubernetes, Http, Avro, Twitter, Cassandra, Kafka Sinks, MongoDB, Load Testing, Kibana, H2O, JSON, Spring Boot, Recommendation Engine, Tensor, MySQL, Logstash, Kafka Streams, Spring, Bayesian, backend, javascript, RabbitMQ, Hibernate, ORM, Batch processing, smart phones, JSPs, JSF, CSS, SyncML, IMAP, XMPP, ActiveSync, Lucene, NLP, UIMA, text extraction

Sprachen

DeutschverhandlungssicherEnglischverhandlungssicher

Projekthistorie

Lead Data Engineer

MHP/Porsche AG

Automobil und Fahrzeugbau

Design and Implementation of Data Ingestion Pipelines, batch and streaming data products using Kafka, Spark, Airflow, AWS

Solution Architect/Lead Engineer

Daimler AG

Automobil und Fahrzeugbau

>10.000 Mitarbeiter

Description: Scala/Java based Data Ingestion pipeline to ingest and transform CDC
data from Oracle into different Data Sinks.

* Consuming CDC data from Oracle Database by means of Oracle Golden Gate
Connector.
* Kafka used as streaming platform to stream CDC changes.
* Confluent Schema Registry used to provide Schema to Data mapping.
* KStreams and Spark Streaming used to perform enrichment of incoming data.
* Data is streamed into S3 for storage and analysis by Athena.
* Metrics and Monitoring via Prometheus and Grafana.

* Streamed data on Kafka is used in real-time to populate a Data Vault.
* Data Vault is based on Clouder HDP (Hbase, Hive, Phoenix and Ranger)

Distribution Processing Application (Architect/Lead)

BMW AG
Description: AWS/Java based application to forecast supply and demand across
various locations

* Data from on-premise Kafka is streamed over to Kinesis via AWS Direct
Connect.
* Kafka2Kinesis Connector running on AWS Fargate performs conversion to
Kinesis Data Streams.
* Kinesis Analytics is used for grouping and aggregation of Streaming Data in
real-time.

Terraform * Aggregated and Enriched data is stored in AWS Aurora (Postgres Flavour)
* Dynamo DB is used as a cache and as intermediate storage.
* Enriched Data is visualized using AWS Quick Sight.
LANGUAGES * Power BI can access enriched data via JDBC
* AWS Glue and EMR/Spark used for ETL.
German * Cloud Watch and SNS for logs and alerting.

Lead Engineer

BMW AG
Description: AWS/Java based application that provides Location Search for BMW
connected cars.

* Implementation of infrastructure-as-code using Terraform.
* On-premise to Cloud connectivity via means of AWS Direct Connect and VPCs.
* Microservices based on Java running in AWS Fargate.
* AWS EMR/Spark to pre-process data in batches (ETL Jobs)
* S3 and Rest APIs for third-party integration.
* Dynamo DB and Aurora for persistence.
* API Gateway for securing, throttling and monitoring the APIs.
* Cloud Watch and SNS for logs and alerting.

Architect/Tech Lead

BMW AG
Description: AWS/Java based application that facilitates self-driving of cars at the
factory between stations.

* Realtime positions of cars are obtained as Kinesis Data Streams.
* Based on realtime location and other data points, Lambdas calculate next
location for car to drive to.
* Dynamo DB and Aurora used for persistence.
* AWS Glue and EMR/Spark used to perform ETL jobs on historical data.
* Cloud Watch and SNS for logs and alerting.

Architect/Lead Engineer

AI Building Blocks; BMW
Description: AWS/Java based application that provides ML capabilities and
applying ML models on various events.

* Kstreams applications running on Openshift/K8s consume events from Kafka
* ML Models are applied in real-time and results are sent downstream on Kafka
* ML Model Deployment using KubeFlow.
* ML Model versioning done with Data Version Control (DVC)

Knowledge Graph (Architect/Lead Engineer)

Deloitte Consulting GmbH
Description: AWS/Scala based application that provides an enterprise and
relational search engine.

* Realtime indexing via Kafka Event Streaming.
* Full-text search engine using Elastic Search.
* Relational search using GraphDB (Neo4j).
* Openshift based GraphQL microservices.

Engineer

eBay AG
Description: Java/Scala/Kubernetes based product that enables deployment of
GDPR compliant Data Ingestion pipelines with support for multiple Data Sinks

* Kubernetes based Ingestion as a Service.
* Scala based micro-services as the event proxy (Akka Http)
* Kafka deployed on K8s with the Strimzi operator.
* Avro schema based validator and transformers (for PII Data).
* Twitter algebird used for Bloom Filters (GDPR features)
* Support for Hive, S3, Cassandra and Kafka Sinks.
* Metrics and Monitoring via Prometheus and Grafana

User Profiles (Architect/Lead Engineer)

eBay AG
Description: Scala/Spark based application which generates User Profiles in near
real-time and serving up-to 5000 requests/second with < 50ms latency

* Click Event Stream consumed from Kafka.
* Spark Streaming for generating User Profiles in Real-time.
* Spark Batch for generating the "prior" for the User Profiles.
* MongoDB for storage of "prior" and enrichment of Real-time User Profiles.
* Cassandra for storage of User Profiles.
* Scala based micro-services (Akka Http) for aggregating and serving of User
Profiles.
* Load Testing performed with Gattling.
* Kibana Dashboards for additional metrics.
* Metrics and Monitoring via Prometheus and Grafana

Price Transparency (Lead Engineer)

eBay AG
Description: Scala/Kafka based application which applies ML Models on incoming
data and provides price-labels.

* Listing Change events consumed from Kafka.
* ML Model from Data Scientists trained using H2O.ai deployed as a JSON file
of attributes (Random Forests) and stored in MongoDB




* Kstreams to enrich incoming Ad Listings with ML model and tagging with
Price Categories.
* Scala based micro-services (Akka Http) for serving Price
Transparency/Average Price information.
* Kibana Dashboards for additional metrics.
* Metrics and Monitoring via Prometheus and Grafana

Architect/Lead Engineer

eBay AG
Description: Java/Scala/Spark based framework which extends existing Data
capabilities with GDPR features.

* Design and implementation of a platform to perform GDPR (right to access &
right to be forgotten).
* Bloom Filters for Historical Data search.
* Tombstones in Cassandra for user request to be forgotten.
* Spring Boot based micro-services for serving layer.
* Metrics and Monitoring via Prometheus and Grafana

Architect/Lead Engineer)

Personalized Recommendation Engine; eBay AG
Description: Java/Elastic Search based application which which combines User
Profiles and Item attributes to provide User Recommendations

* Combines User Profiles stored in Cassandra together with Listing attributes
from Elastic Search.
* Serving Tensor Flow models via Tensor Serving.
* Increase in conversion rate by 7%
* User Profile "prior" trained by Data Scientists stored in MongoDB
* Design of Elastic Search Schema with respect to User Profile to achieve <50ms
latency for up-to 1600 requests/second.
* Modeling Elastic Search queries applying decays and preferences of User
Profile.
* Spring Boot based micro-services for serving.
* Kibana Dashboards for additional metrics.
* Load Testing performed with Gattling.
* Metrics and Monitoring via Prometheus and Grafana


Recommendation Engine Tuning and LTR (Lead Berlin

Engineer

eBay AG
Description: Java/Elastic Search based application which provides Item based
recommendations and serving up-to 1600 requests/second with <50ms latency

* Listing attribute based Recommendation Engine combined with Listing
similarity (Item-Item recommendations)
* Increase in conversion rate by 3%
* ML Model for weights provided by Data Scientists.
* Translation of Attribute weighted model to Elastic Search Query and scaling
out to up-to 1600 requests/second in Production with <50ms latency.
* Automatic weight deduction based on LearnToRank (LTR) and past truth data.
* Spring Boot based micro-services for serving layer.
* Performance metrics on Kibana.
* Metrics and Monitoring via Prometheus and Grafana.

Dealer Profiles (Senior Engineer)

eBay AG
Description: Scala/Spark based application which generates Dealer Profiles in near
real-time and serving up-to 5000 requests/second with < 50ms latency

* Design and Implementation of Dealer Profiles for car dealers.
* Kafka for real-time ingestion and processing of dealer information.
* Spark Streaming for calculation of Dealer Profiles in near real-time
* MySQL and Cassandra for persistence.
* Logstash for indexing profiles into Elastic Search
* Metrics and Monitoring via Prometheus and Grafana.

Recent User Activity (Senior Engineer)

eBay AG
Description: Scala/Cassandra/Kafka based application which persists User Activity
in near real-time and serving up-to 5000 requests/second with < 50ms latency

* Senior Backend Engineer in implementation of Recent User Activity storage.
* Kafka Streams for real-time ingestion of User Activity.
* Cassandra and PostGres for persistence.
* Spring Boot for micro-services serving layer.
* Metrics and Monitoring via Prometheus and Grafana.

Fraud Detection

Random Forests, eBay AG
Description: Java/Spring based application which applies trained ML models on
incoming listing data and classifying them as fraudulent/non-fraudulent

* Implementation of a H2O.ai based Fraud Detection System.
* Serving of Machine Learning models via H2O.ai Pojos.
* Micro-services using Spring Boot.
* Tracking model precision and recall via Kibana.
* Metrics and Monitoring via Prometheus and Grafana.

Customer Support Tools

eBay AG
Description: Implementation of various Customer Support tools for detecting
fraud.

* Java/Spring based.
* Hibernate as ORM and MySQL, Mongodb for persistence.
* RabbitMQ for the messaging layer.
* Metrics and Monitoring via Prometheus and Grafana

Sell-Your

eBay AG
Description :Implementation of the Sell-your-item workflow for selling used cars.

* Java/Spring based.
* Hibernate as ORM and MySQL, Mongodb for persistence.
* Metrics and Monitoring via Prometheus and Grafana.

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