Senior Software Engineer

Chennai, State of Tamil Nādu, Indien Global Business Services 59265 2. February, 2021
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• Practical experience with public clouds (preferred: GCP, Azure), with specific focus on using them for Data Lakes, e. g.: 
GCP: BigQuery, Dataproc, Dataflow, KNative, …;Azure: Synapse (Analytics), Databricks, HDInsight, EventHub, Functions, … 
• In-depth practical experience with implementing Big Data ingestion pipelines feeding into a Data Lake, applying best practices for transformation, deduplication, enrichment, anonymization, schema lifecycle management, etc. 
Ingestion for batch and near-realtime streaming mode using a combination of e. g. (but not limited to): 
Airflow, Spark, Spark Streaming, Flink, Beam, Kafka, kafka-connect 
• In depth practical experience with at least 1 of the following SQL DBs / NoSQL DBs / data processing and storage components. Ability to tune for applications, troubleshoot, apply best practices during the complete lifecycle of projects: 
ElasticSearch, HBase, Cassandra, Drill, Hive, Impala, Presto, Kudu, MongoDB 
• In-depth practical experience with at least 1 of the following programming languages and willingness to get familiar with 1-2 additional ones out of this list. Competence for guiding a development team to apply best practices and a suitable application design using the respective programming language. Basic experience with functional programming or willingness to become immersed to a moderate degree: 
Python, Scala, Java, C++, Rust, Go, C#, F# 
• Practical experience with Docker and Kubernetes and related ecosystem tooling on-prem and in public clouds. 
• Basic practical experience (from a user’s perspective) with automation and CI/CD tooling for on-prem and in public clouds, e. g.: 
Jenkins, Flux, Terraform, Github Enterprise, Ansible 

Optional, for additional bonus points, experience with: 
• Best-practices and patterns for software architecture for data-intensive systems 
• Microservices and service meshs on Kubernetes 
• Kubeflow engineering (from a Data Engineer’s perspective, not from a Data Scientist’s perspective) 
• A Hadoop vendor distribution (MapR, Cloudera) 
• DB change data capture (CDC) products and tools 
• PowerBI 
• Kibana 
• Grafana 
• Prometheus 
• Stateful applications and data-intensive components (e. g. ElasticSearch, MongoDB, Spark, …) in Kubernetes

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