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Senior Data Engineer (with AWS)

We are seeking a talented and experienced Data Engineer to join our team at Provectus. As part of our diverse practices, including Data, Machine Learning, DevOps, Application Development, and QA, you will collaborate with a multidisciplinary team of data engineers, machine learning engineers, and application developers. You will encounter numerous technical challenges and have the opportunity to contribute to Provectus’ open source projects, build internal solutions, and engage in R&D activities, providing an excellent environment for professional growth.


Requirements
  • Experience in data engineering;
  • Experience working with Cloud Solutions (preferably AWS, also GCP or Azure);
  • Experience with Cloud Data Platforms (e.g., Snowflake, Databricks);
  • Proficiency with Infrastructure as Code (IaC) technologies like Terraform or AWS CloudFormation;
  • Experience handling real-time and batch data flow and data warehousing with tools and technologies like Airflow, Dagster, Kafka, Apache Druid, Spark, dbt, etc.;
  • Proficiency in programming languages relevant to data engineering such as Python and SQL;
  • Experience in building scalable APIs;
  • Experience in building Generative AI Applications (e.g., chatbots, RAG systems);
  • Familiarity with Data Governance aspects like Quality, Discovery, Lineage, Security, Business Glossary, Modeling, Master Data, and Cost Optimization;
  • Advanced or Fluent English skills;
  • Strong problem-solving skills and the ability to work collaboratively in a fast-paced environment.


Nice to Have:
  • Relevant AWS, GCP, Azure, Databricks certifications;
  • Knowledge of BI Tools (Power BI, QuickSight, Looker, Tableau, etc.);
  • Experience in building Data Solutions in a Data Mesh architecture;
  • Familiarity with classical Machine Learning tasks and tools (e.g., OCR, AWS SageMaker, MLFlow, etc.).


Responsibilities:
  • Collaborate closely with clients to deeply understand their existing IT environments, applications, business requirements, and digital transformation goals;
  • Collect and manage large volumes of varied data sets;
  • Work directly with Data Scientists and ML Engineers to create robust and resilient data pipelines that feed Data Products;
  • Define data models that integrate disparate data across the organization;
  • Design, implement, and maintain ETL/ELT data pipelines;
  • Perform data transformations using tools such as Spark, Trino, and AWS Athena to handle large volumes of data efficiently;
  • Develop, continuously test and deploy Data API Products with Python and frameworks like Flask or FastAPI.


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Average salary estimate

$110000 / YEARLY (est.)
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max
$90000K
$130000K

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What You Should Know About Senior Data Engineer (with AWS), Provectus

At Provectus, we're excited to expand our fantastic team with the addition of a Senior Data Engineer! If you're passionate about data engineering and have a flair for using AWS, this position might be your perfect fit. As part of our dynamic and collaborative environment, you'll work alongside brilliant minds across various disciplines, including data engineers, machine learning engineers, and application developers. You’ll dive into thrilling technical challenges while contributing to open source projects, developing internal solutions, and participating in innovative R&D activities that promise to enhance your professional journey. In this role, you'll need to bring your experience in cloud solutions - especially AWS, but also GCP and Azure can be beneficial. We value your proficiency in tools like Snowflake, Databricks, and your ability to construct scalable APIs. If you’re familiar with Terraform or AWS CloudFormation, that’s a significant bonus! You will have the chance to build resilient data pipelines and engage with data governance aspects, all while fostering collaboration with clients to tailor solutions to their unique needs. This is ideal for someone eager to get hands-on with real-time and batch data flows and tackle exciting projects in a fast-paced environment. Bring your strong problem-solving skills, a mindset for innovation, and an eagerness to learn as you help drive the success of our data initiatives at Provectus!

Frequently Asked Questions (FAQs) for Senior Data Engineer (with AWS) Role at Provectus
What responsibilities does a Senior Data Engineer at Provectus have?

As a Senior Data Engineer at Provectus, your core responsibilities encompass collaborating closely with clients to grasp their IT environments and business needs. You will collect and manage diverse data sets, work alongside Data Scientists and ML Engineers to develop resilient data pipelines, and design data models that integrate various data sources. Additionally, you’ll implement ETL/ELT pipelines and perform data transformations using advanced tools like Spark and AWS Athena.

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What qualifications are required to become a Senior Data Engineer at Provectus?

To qualify for the Senior Data Engineer position at Provectus, candidates should have substantial experience in data engineering, particularly with cloud solutions like AWS, GCP, or Azure. Proficiency in data platforms such as Snowflake or Databricks, programming skills in Python and SQL, and experience with Infrastructure as Code technologies like Terraform are essential. Fluency in English and strong problem-solving abilities are also key.

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What programming languages should a Senior Data Engineer at Provectus be familiar with?

A Senior Data Engineer at Provectus should be well-versed in relevant programming languages such as Python and SQL. Proficiency in these languages is vital for developing data pipelines, creating APIs, and conducting data transformations efficiently. Experience with additional languages or technologies relevant to data engineering can further enhance your qualifications.

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What cloud platforms do Senior Data Engineers at Provectus work with?

At Provectus, Senior Data Engineers primarily work with AWS, but familiarity with other cloud platforms like GCP or Azure is also beneficial. Your experience with cloud data platforms and related tools will play a crucial role in implementing scalable data solutions and optimizing performance for our clients.

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What opportunities for growth does a Senior Data Engineer at Provectus have?

Working as a Senior Data Engineer at Provectus opens up numerous opportunities for professional growth. You'll engage in exciting R&D activities, contribute to open-source projects, and collaborate with a multidisciplinary team. This dynamic environment encourages continuous learning and skill development, preparing you for advanced roles in data engineering and beyond.

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Common Interview Questions for Senior Data Engineer (with AWS)
Can you explain the importance of data pipelines in data engineering?

In data engineering, data pipelines are crucial for automating the flow of data from various sources to storage and processing systems. They ensure that data is available for analysis and business intelligence in a timely manner. When responding, highlight your experience with designing and implementing efficient pipelines, and mention any specific tools you've used, such as Apache Airflow or AWS Glue.

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What challenges have you faced while handling real-time data flows?

Discussing challenges in handling real-time data flows showcases your problem-solving skills. You might mention issues related to data latency, ensuring data quality, or managing system scalability. Share specific examples of how you addressed these challenges, emphasizing technical solutions and the outcome.

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How do you ensure data quality and governance in your projects?

Data quality and governance are essential in any data engineering role. You can mention practices such as implementing validation checks during data ingestion, establishing data lineage, and using monitoring tools to track data quality over time. Sharing your familiarity with data governance frameworks can also enhance your response.

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What is your approach to collaborating with Data Scientists and ML Engineers?

Collaboration with Data Scientists and ML Engineers is key in ensuring that data pipelines meet the needs of predictive modeling and machine learning applications. Highlight your communication skills and willingness to understand their requirements. Describe a previous project where collaboration led to successful outcomes, demonstrating your teamwork abilities.

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Describe your experience with Infrastructure as Code (IaC).

Detail your experience with IaC, particularly tools like Terraform or AWS CloudFormation. Discuss how IaC practices have helped automate infrastructure management in your data engineering projects, emphasizing improvements in deployment speed and consistency.

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How do you stay updated on new data technologies and tools?

Staying updated in the fast-evolving field of data engineering is essential. Mention resources you use, such as online courses, webinars, professional communities, or industry events. Emphasize your commitment to continuous learning and adapting to new technologies to stay competitive in your role.

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What role does ETL/ELT play in your data engineering workflows?

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are foundational concepts in data engineering, determining how data is moved and processed. Provide examples of workflows you've designed, explaining when to use ETL vs. ELT based on factors like data volume and source format.

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Can you share a project where you built a scalable API?

Discussing a project involving API development demonstrates your experience with creating scalable solutions. Describe the purpose of the API, the technologies you used (like Flask or FastAPI), and how you ensured scalability and security. Highlight how the API delivered value to stakeholders.

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How do you handle data from disparate sources?

Integrating data from disparate sources is a common challenge in data engineering. You can elaborate on your approach to normalizing data formats, using data integration tools, and validating the accuracy of combined data sets. Mention any successful projects where you've implemented these strategies.

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What is your understanding of Data Governance principles?

Data Governance principles are crucial for managing data compliance, quality, and security. Discuss concepts like data lineage, data stewardship, and accountability. Sharing your experience or familiarity with industry standards will demonstrate your understanding of this vital aspect of data engineering.

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Full-time, remote
DATE POSTED
April 19, 2025

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