Data Engineer

Work Force

Standszám:

Tanácsadó Műhely

Full remote!
Not just an employment relationship!
Also with a contract of assignment!

About the Company:

The Marketing Technology (MarTech) team is part of the Customer Experience (CX) product development organization responsible for building tools and processes allowing marketers to craft frictionless buyers journeys for our partner’s vast portfolio of infrastructure products and business applications. The MarTech team maintains the infrastructure, integration and processes aiding marketers from data capture all the way to ML applications and everything in between.

About the team:

The Data Science team is part of MarTech and consists of data engineers, data scientists and ML engineers. Their job is to deliver data products and ML applications that improve productivity, deliver insights, and allow marketing and business development teams to engage their audiences with timely and relevant messaging. They prototype and build their solutions for their marketing teams with the objective of incorporating them into CX portfolio of products.

Their stakeholders and collaborators include teams responsible for marketing their products, infrastructure, and instrumentation as well as other data science teams. They also work with a broad array of marketing technology and data vendors to improve their data and capabilities.

What they Do?

They work with large volume, transaction level data (including clickstream, demographics, firmographics, marketing responses, purchase and business transactions, and service consumption) and operate on a global scale, leveraging their Cloud Infrastructure services. As a full-stack data team they explore, process, cleanse, standardize data to calculate features for their ML efforts, maintain/customize their own tooling and infrastructure to advance MLOps initiatives and strive for engineering excellence across the board. They also provide low level ML tooling for other data science teams and consult on technology/process integration. Part of their work is focused on building and maintaining data foundations around identity/entity resolution, taxonomy curation, data augmentation, and system integration – all of which requires strong engineering and data skills as well as understanding of the B2B enterprise technology domain. They use their infrastructure and data foundation elements to build data products and ML applications that their customers, the marketers interface with directly. Their applications include account and contact level targeting/segmentation, ranking/prioritization, experimentation, measurement, attribution, personalization, content recommendation, and forecasting.

If you are:

A collaborative individual who prioritizes progress over perfection, with a mentality of “never stop learning”. Someone who has developed the ability to actively listen — both to colleagues and customers – as well as to lead. You are an individual that builds trust over the course of challenging projects and is dependable to persist through unforeseen obstacles. You are a seasoned professional ready to pick up more and more about the B2B enterprise software domain with a drive to deliver results.

You are int the right place!

What tasks await the future colleague?

In this role, you’ll be working with data scientists and ML engineers on the team as well as other stakeholders to make sure the upstream data reaches the components we own at the right cadence, quality and latency and similarly it gets to its destination system in a reliable manner.

Develop and design data pipelines for internal and external data sources to support new product launches and drive data quality across data products.
Build and own the automation and monitoring frameworks that capture metrics and operational KPIs for data pipeline quality and performance.
Implement best practices around systems design, data modeling, integration, security, performance and data management.
Collaborate with teammates and stakeholders to identify, define, document and deliver solutions.
Identify opportunities to standardize and streamline implementation of engineering excellence and adopt industry best practices.
Contribute to turning data science prototypes into production grade data products
Create documentation of processes & tools and to write playbooks and how-to guides.

What are the expectations for the job?

Bachelor’s (ideally M.S.) degree in Computer Science, Computer
Engineering, Data Science, or related field.

Advanced SQL knowledge including performance tuning
Strong in Oracle PL/SQL:
Language fundamentals
Data types
Control statements
Procedures & functions
Dynamic SQL
Python, Pandas, shell scripts

What do we offer?

Dynamic atmosphere
Constant challenges
Good mood
The possibility of personal development
Friendly work environment
Flexible working hours
Remote work
Birthday day off

How to apply: at booth b13

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