Lead, Data Engineer

Lead, Data Engineer  - 0098TK

Job Profile – Lead Data Engineer

Does working with data on a day to day basis excite you? Are you interested in building robust data architecture to identify data patterns and optimise data consumption for our customers, who will forecast and predict what actions to undertake based on data? If this is what excites you, then you’ll love working in our intelligent automation team.

 

Schneider AI Hub is leading the AI transformation of Schneider Electric by building AI-powered solutions. We are looking for a savvy Data Engineer to join our growing team of AI and machine learning experts. You will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up.

 

The Data Engineer will support our software engineers, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.

Responsibilities

·       Create and maintain optimal data pipeline architecture; assemble large, complex data sets that meet functional / non-functional requirements.

·       Design the right schema to support the functional requirement and consumption patter.

·       Design and build production data pipelines from ingestion to consumption.

·       Create necessary preprocessing and postprocessing for various forms of data for training/ retraining and inference ingestions as required.

·       Create data visualization and business intelligence tools for stakeholders and data scientists for necessary business/ solution insights.

·       Identify, design, and implement internal process improvements: automating manual data processes, optimizing data delivery, etc.

·       Ensure our data is separated and secure across national boundaries through multiple data centers Requirements and Skills

·       You should have a bachelors or master’s degree in computer science, Information Technology or other quantitative fields

·       You should have at least 8 years working as a data engineer in supporting large data transformation initiatives related to machine learning, with experience in building and optimizing pipelines and data sets

·       Strong analytic skills related to working with unstructured datasets.

·       Experience with Azure cloud services, ADF, ADLS, HDInsight, Data Bricks, App Insights etc

·       Experience in handling ETL’s using Spark.

·       Experience with object-oriented/object function scripting languages: Python, Pyspark, etc.

·       Experience with big data tools: Hadoop, Spark, Kafka, etc.

·       Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.

·       You should be a good team player and committed for the success of team and overall project. 

About Us

Schneider Electric™ creates connected technologies that reshape industries, transform cities and enrich lives. Our 144,000 employees thrive in more than 100 countries. From the simplest of switches to complex operational systems, our technology, software and services improve the way our customers manage and automate their operations.

 

Great people make Schneider Electric a great company.

 

We seek out and reward people for putting the customer first, being disruptive to the status quo, embracing different perspectives, continuously learning, and acting like owners. We want our employees to reflect the diversity of the communities in which we operate. We welcome people as they are, creating an inclusive culture where all forms of diversity are seen as a real value for the company.  We’re looking for people with a passion for success — on the job and beyond.

 

Primary Location

: IN-Karnataka-Bangalore

Schedule

: Full-time

Unposting Date

: Ongoing