Senior Data Engineer @ Proxet

1 week ago

Remote Wroclaw, Czech Republic Proxet Full time
  • Possess a Bachelor’s degree in Computer Science or Software Engineering, or demonstrate substantial experience as a seasoned app developer.
  • Demonstrate a minimum of 3 years of proficiency in Python, SQL, data systems development life cycle.
  • Experience using different kinds of databases (for example, PostgreSQL, BigQuery, Redis) including experience with query and optimization techniques.
  • Display a comprehensive understanding and practical experience with Google Cloud services.
  • Experience with unit and integration testing methodologies.
  • Hands-on experience working with Docker
  • Proven background in collaborative efforts with product managers and fellow engineers, particularly within distributed multicultural teams.
  • An excellent command of the English language, both written and verbal.
  • Possess outstanding communication skills, coupled with a sense of humor, and express a keen interest in the domains of personal and corporate finance.

Nice to have

  • Experience with Airflow
  • Experience with Airbyte and its integration with data platforms.
  • Familiarity with Google Cloud
  • Familiarity with data observability tools like Monte Carlo Data and data discovery tools like Open Metadata.
  • Knowledge of the financial domain and understanding of wealth management, and investment concepts.
  • Contributions to open-source projects or personal projects showcasing data engineering skills.

We are seeking a skilled Data Engineer to build and maintain our data warehouse and efficiently process data from various 3rd party platforms, ensuring it is transformed into well-structured data products for further business use. You will play a pivotal role in shaping the data infrastructure for a finance-related project, focusing on transforming raw data into reliable, accessible formats that meet the organization’s needs.

As a Data Engineer, you will design and maintain scalable data pipelines that ingest and transform data from external platforms. You’ll ensure data quality, reliability, and availability to support business requirements. You will collaborate directly with engineering, product, and business teams to understand their data needs and deliver tailored technical solutions that address those requirements.

We are seeking a proactive individual who can be responsible for a big part of our data platform, drive innovation, and continuously enhance the data architecture to support the growing needs of the business. Your ability to effectively communicate technical concepts, provide constructive feedback, and facilitate knowledge sharing among team members at all levels will be highly valued.

About the project:

The client is a renowned venture capital firm that manages its fund using Salesforce as the central data repository, alongside various other platforms for additional data sources. These sources range from Excel spreadsheets to external commercial tools and custom-built user-facing applications. Different departments rely on multiple siloed systems for data management, leading to a fragmented data landscape.

The major challenge is the suboptimal time to make the final decision as analysts can't rely on the existing data due to possible human errors and complicated data management. The solution is to build a unified data platform that will be integrated with Salesforce, fund accounting, and other existing systems to efficiently transform financial, legal, and ops data to data marts that are ready for interaction and comprehensive visualization with BI tools on the web and mobile platforms.

Tech stack: Google Cloud, BigQuery, GCS, DBT, SQL, Airbyte, Airflow, Python, FastAPI, Pandas, Terraform, GitHub Actions, Sigma Computing, Open Metadata, Monte Carlo Data, OpenAI, LLMs

Location: Remote Poland/Europe

,[Collaborate with cross-functional teams to understand business requirements , Design, implement, and maintain scalable and reliable data pipelines, data warehouses, and data lakes. , Develop and enforce best practices for data governance, data quality, and data security. , Help maintain code quality, organization, and automation. , Collaborate with other teams as needed to ensure interoperability, stability, and code reusability. , Optimize data processing and querying for better performance and cost-efficiency. , Stay up-to-date with the latest trends and technologies in the data engineering field (Modern Data Stack) and propose improvements to the existing architecture. ] Requirements: Data warehouse, Data pipelines, Google cloud, BigQuery, dbt, SQL, Python, pandas, PostgreSQL, Redis, Docker, Communication skills, Data engineering, Data warehouses, Data Lake, BI, Terraform, FastAPI, Airflow Additionally: Knowledge sharing.

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