Job Description

Our Deep Learning and Analytics team is looking for a talented, enthusiastic and passionate Big Data/Data Engineer with a strategic and innovation mindset to help us shape and deliver our Data Analytics vision.

Why is working with us interesting?

We’re a global strategy team part of a global CIO family that works with Big Data, Cloud and cutting-edge Data Science technologies for Accenture’s internal IT infrastructure in the connectivity, network and compute services area.

Due to our scale, we ingest data streams from around the globe and process terabytes of data, delivering cutting edge Analytics solutions but also managing hyper scale Data Lake.

What will be your job?

You will design, develop, deploy and test a range of end-to-end data pipelines that will enable our visualization and data scientists to perform predictive and prescriptive analytics at scale. Your data plumbing work will be key.

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Qualifications

  • MSc. program or university degree (preferably in a quantitative discipline such as Mathematics, Physics, Statistics, Econometrics, Computer Science).

  • Hands-on Java, Python, C#, Spark, SQL, Linux.

  • Write complex SQL queries to extract data from data lake.

  • Hands-on Azure Data engineering related services (Azure Data Factory, EventHub/grid, Databricks, stream Analytics, Synapse…) or with similar services in other cloud vendors.

  • Experience in Building fault-tolerant, scalable batch and real-time distributed data processing systems an associated technology in the cloud.

  • Experience processing and transforming unstructured/structured data at scale in optimal ways (cost /performance).

  • Optimize performance and resource utilization on big data cloud production environments.

  • Building and working with RESTful APIs and / or data streaming.

  • Knowledge of relational and non-relational databases, data modeling and big data frameworks.

  • A desire to learn, embrace new skills and a positive attitude towards work and life.

  • Good English; written and verbal communication skills.

Nice to have skills:

  • Certified Microsoft data engineer or willing to obtain it.

  • Experience analyzing big data in magnitudes or Terabytes /petabytes preferably coming from ITSM logs, events, IoT tools at scale.

  • Enjoy discovering, testing and researching new big data technologies and being able to share results and best practices via whitepapers, blogs, townhalls...

  • General understanding of Knowledge about Data Mining, Statistics and Machine Learning.

  • Experience deploying microservices such as Kubernetes, docker...

  • Basic understanding of Agile and Design Thinking methodologies.

  • Experience with infrastructure related Analytics would be a plus.

  • General knowledge about Agile and DevOps delivery frameworks.

What you can expect

  • You’ll have fun and be part of a pleasant and multicultural team with very good working life balance.

  • Within our growing infrastructure analytics team, you’ll work in diverse and interesting projects at scale with a global impact.

  • There is an exciting career progression, providing great growth opportunities, learning & development possibilities (Pluralsight, Skillsoft’s, conferences…) 

  • Home office or office as preferred.

  • Mobile data and home internet plan.

All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.

Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.

Accenture is committed to providing veteran employment opportunities to our service men and women.

COVID-19 update:  The safety and well-being of our candidates, our people and their families continues to be a top priority. Until travel restrictions change, interviews will continue to be conducted virtually. 

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