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As a Principal Data Engineer, you will build and maintain Enterprise Level Data Pipelines utilizing the tools available within our Big Data Eco-System. This will require you to work closely with data analysts and scientists, and database and systems administrators to create data solutions. You will collaborate with business and technical teams to translate business requirements and functional specifications into innovative solutions. This will require research, awareness, interactivity, and the ability to ask the right questions. You will also be responsible for serving as a technical expert for project teams throughout the implementation and maintenance of business and enterprise software solutions, and in addition, you will provide consultation to help ensure new and existing software solutions are developed with insight into industry best practices, strategies, and architectures and pursues professional growth.

Design, build, and deploy new data pipelines within our Big Data Eco-Systems using Streamsets/Talend/Informatica BDM etc. Document new/existing pipelines, Datasets.

Design ETL/ELT data pipelines using StreamSets, Informatica or any other ETL processing engine. Familiarity with Data Pipelines, Data Lakes and modern Data Warehousing practices (virtual data warehouse, push down analytics etc.)

Expert level programming skills on Python

Expert level programming skills on Spark

Cloud Based Infrastructure: AWS (and the very many services it offers) i.e. EC2, RDS, AWS Redshift, EMR, Snowflake, Athena, PrestoDB

Experience with one of the ETL Informatica, StreamSets in creation of complex parallel loads, Cluster Batch Execution and dependency creation using Jobs/Topologies/Workflows etc.,

Experience in SQL and conversion of SQL stored procedures into Informatica/StreamSets, Strong exposure working with web service origins/targets/processors/executors, XML/JSON Sources and Restful API’s.

Strong exposure working with relation databases DB2, Oracle & SQL Server including complex SQL constructs and DDL generation.

Exposure to Apache Airflow for scheduling jobs

Strong knowledge of Big data Architecture (HDFS), Cluster installation, configuration, monitoring, cluster security, cluster resources management, maintenance and performance tuning

Create detailed designs and POCs to enable new workloads and technical capabilities on the Platform.  

Work with the platform and infrastructure engineers to implement these capabilities in production.

Manage workloads and enable workload optimization including managing resource allocation and scheduling across multiple tenants to fulfill SLAs.

Participate in planning activities, Data Science and perform activities to increase platform skills

Minimum 6 years of experience in ETL/ELT Technologies, preferably StreamSets/Informatica/Talend etc.,

Minimum of 6 years hands-on experience with Big Data technologies e.g. Hadoop, Spark, Hive.

Minimum 3+ years of experience on Spark

Hands on experience with Databricks is a HUGE plus.

Minimum 6 years of experience in Cloud environments, preferably AWS

Minimum of 4 years working in a Big Data service delivery (or equivalent) roles focusing on the following disciplines:

AWS S3 Creating Buckets/Tokens etc.,

Big Data (Hadoop ecosystems/distributions e.g. Cloudera 5.14 or 5.15, Databricks)

Any experience with NoSQL and Graph databases

Informatica or StreamSets Data integration (ETL/ELT)

Exposure to role and attribute based access controls

Exposure to BI tools like Tableau, PowerBI, Looker, etc.,

Hands on experience with managing solutions deployed in the Cloud, preferably on AWS

Experience working in a Global company, working in a DevOps model is a plus