Data Scientists for Ericsson
Ericsson is looking for savvy Data Engineers to join our growing team of MI experts. As a team member, you will be evolving and optimizing our data and data pipeline architecture, as well as, optimizing data flow and collection for cross functional teams. You are an expert data pipeline builder and data wrangler who enjoys optimizing data systems and evolving them. The Data Engineer will support our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data and models devOps (dataOps) architecture is consistent throughout ongoing projects. You are self-directed and comfortable supporting the dataOps needs of multiple teams, systems and products. You will also be responsible for integrating them with the architecture used across the company. The right candidate will be excited by the prospect of optimizing or even re-designing our company’s dataOps architecture to support our existing and next generation of MI-driven products and solutions initiatives.
Your Roles and Responsibilities
What we would like to see
- Gaining a good understanding of business processes and domain knowledge by working with stakeholders including the Executive, Product, Data and Design teams
- Contributing to the data warehouse design and data preparation by implementing a solid, robust, extensible design that supports key business flows.
- Assist with the creation and maintaining of complex data pipelines
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and cloud-based ‘big data’ technologies from AWS, Azure and others.
- Keep data separated and secure across national boundaries through multiple data centers and strategic customers/partners.
- Create tool-chains for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
- Work with data and machine learning experts to strive for greater functionality in our data and model life cycle management systems.
- Build sanity checks and dashboards for monitoring data quality, pipeline performances and infrastructure health.
- Support DataOps competence build-up in Ericsson Businesses and Customer Serving Units
- BS, MS or PhD degree in Computer Science, Informatics, Information Systems or another related field.
- 0-2 years’ experience using the following software/tools:
- Familiarity with NoSQL databases such as Cassandra, Solr, MongoDB, etc..
- Experience with tools/software for big data processing such as Hadoop, Spark
- Experience with handling data streams with tools such as Flink, Spark-Streaming, Kafka or Storm
- Experience with Data and Model pipeline and workflow management tools such as Azkaban, Luigi. Airflow or Dataiku.
- Experience with Docker containers, orchestration systems (e.g. Kubernetes), continuous integration and job schedulers.
- Knowledge of serverless architectures (e.g. Lambda, Kinesis, Glue).
- Experience with microservices and REST APIs.
- Expert knowledge in SQL and traditional RDBMS systems
- Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc. (Advanced level in one language at least)
- Experience supporting and working with cross-functional teams in a dynamic environment
- Advanced SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of other databases/date-sources.
- Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and seek opportunities for improvement.
- Strong analytic skills related to working with unstructured datasets.
- Have built processes supporting data transformation, data structures, metadata, dependency and workload management.
- Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
- Experience in Data warehouse design and dimensional modeling
- Familiar with agile development and lean principles.
- Contributor or owner of GitHub repo.