Data Engineer, AWS Workforce Planning

2 weeks ago

Cape Town, South Africa Amazon Full time

Job ID: 2792821 | Amazon.com Services LLC

Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success.

Come join the team that owns the technology behind key AWS people planning products, services, and metrics. We leverage technology to improve the experience of AWS Executives, HR/Recruiting/Finance leaders, and internal AWS planning partners.

As a Data Engineer on the AWS PXT Workforce Planning team, you will play a critical role in building and maintaining the data pipelines and ETL mechanisms necessary to scale and implement the workforce planning data services and backend architecture. You will work closely with partner teams to design, develop, test, and deploy scalable data solutions that enable efficient workforce planning and decision-making.

You will work closely within a small team of Engineers and Scientists to develop scalable and secure workflows in workforce planning, ML modeling, analysis and inspection. You will enable a global staff of workforce strategists and cross functional groups to perform strategic planning, scenario analysis and understand the downstream impact of workforce decisions. As a data engineer, you will strive to optimize and improve our data infrastructure to be modular, extensible, reusable, secure, scalable, and fault-tolerant.

Key job responsibilities

This position is right for you if you are passionate about solving the ambiguous data architecture challenges on behalf of our customers and users. Being deeply customer focused is a must as you constantly look for ways to improve the quality, speed, maintainability and efficiency of our organization’s data architecture. You are able to think creatively, operate best within an agile team, and are able to proactively anticipate customer needs.

This Role will:

1. Actively collaborate with our product managers and software engineers to understand requirements and acceptance criteria.
2. Own development and maintenance of new and existing scalable, secure, extensible, and reusable data pipelines.
3. Utilize best engineering practices including but not limited to:

1. Robust testing (unit, data integrity, and smoke) with automated rollbacks
2. Linting
3. Secure by default designs
4. Code reviews

5. Engage in collaborative design reviews with fellow engineers consisting of peer data, software, and business intelligence.
6. Collaborate with senior staff to determine data typing, table schemes, and indexing methodologies.
7. Collaborate with partner data engineering teams within peer organizations for shared project initiatives.
8. Consider and advise on key design and technology trade-offs related to data architecture, technology platforms, and table designs.

About the team

Our team communicates who we are as an employer – what it’s like to be an Amazonian, why we love innovating on behalf of customers and why people should join us. We build trust with our partners, dive deep into our data, and love to learn and be curious as we deliver results. Our job is to bring that to life.

Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Inclusive Team Culture

Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship and Career Growth

We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

BASIC QUALIFICATIONS

- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Experience building data pipelines or automated ETL processes
- Knowledge of AWS Infrastructure

PREFERRED QUALIFICATIONS

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
- Knowledge of software engineering best practices across the development life cycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Bachelor's degree in computer science, computer engineering, or related field
- Knowledge of BI analytics, reporting or visualization tools like Tableau, AWS QuickSight, Cognos or other third-party tools

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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