About the role:
The Data Engineer will join the Data Intelligence team to help modernize, scale, and operate the enterprise data platform. This role will build and deliver reliable data pipelines and reusable data products using Snowflake, dbt, Airflow and Astronomer, AWS, SQL, and Python.
The role spans the full data engineering lifecycle, from integrating structured, semi-structured, and unstructured data through transformation, deployment, data quality, governance, observability, and production support.
The ideal candidate will combine strong application and data engineering skills with a practical understanding of the cloud infrastructure, compute environments, security controls, and operational practices that support modern data platforms.
This is a hands-on individual-contributor role suited to an engineer who builds and supports production pipelines within established standards, working closely with senior engineers on the team.
About the project:
Our client is a global real estate services company specializing in the management and development of commercial properties. Over the past several years, the organization has made significant strides in systematizing and standardizing its reporting infrastructure and capabilities, utilizing Snowflake, AWS, Sigma, and DataHub. Due to the increased demand for reporting, the organization is seeking a dedicated team to expand capacity and free up existing resources.
Who are we looking for?
Skills & Experience
- Bachelor's degree in Computer Science, Engineering, or related field;
- 5+ years experience working with big data tools and cloud data platforms (preferable AWS)
- Strong knowledge in SQL, database management system and optimization
- Proficiency in Python
- Production experience of ETL/ELT processes and workflow orchestration tools: at least 1 year with Airflow and dbt
- Knowledge of modern data warehousing tools: Snowflake
- Knowledge of AI tool for assisted programing and specific AI features of data platforms
- Production experience with dimensional, analytical or medallion data modeling
- Experience building, scheduling, and troubleshooting workflows in Airflow or a comparable orchestrator.
- Working experience with core AWS services (such as S3, IAM).
- Experience integrating APIs, SFTP, databases, cloud storage, vendor platforms, flat files, and complex Excel sources.
- Experience implementing data-quality tests (for example, dbt tests) and reconciliation checks.
- Experience with Git-based development, pull requests, and CI/CD.
- Strong troubleshooting and clear written communication skills.
Nice to have
- Data engineering experience in asset management, investment management, private markets, real estate, or a related financial-services organization.
- Experience operating Airflow on Astronomer
- Experience with OCR or document extraction from PDFs and images.
- Experience with data-quality or observability tools, data catalogs, or metadata-management solutions.
Responsibilities
- Build, test, deploy, and support data pipelines using Airflow on Astronomer, dbt, Snowflake, SQL, and Python, following team standards and established patterns.
- Develop within the team’s Snowflake architecture, including its database, schema, role, and warehouse conventions, and contribute to performance tuning and cost awareness.
- Develop dbt models across the raw, staging, and mart layers, with tests and documentation.
- Migrate legacy workflows to Airflow and Astronomer using the team’s reusable patterns for dependencies, retries, backfills, alerting, and idempotent processing.
- Troubleshoot DAG and task failures using Airflow logs and the Airflow UI, and escalate platform-level issues.
- Work with AWS services that support the platform, such as S3, IAM, Lambda, and Secrets Manager.
- Build secure and resilient integrations across APIs, SFTP, databases, cloud storage, vendor platforms, flat files, Excel workbooks, and event-based systems.
- Develop ingestion and processing for structured and semi-structured data, such as CSV, JSON, and Excel.
- Implement automated data-quality tests and reconciliation checks, and monitor pipeline health.
- Work in Git-based workflows, including pull requests, code review, automated testing, and environment promotion.
- Follow data classification, access control, and restricted-data handling standards.
- Participate in production support, including triage, troubleshooting, escalation, and contributing to root-cause analysis.
- Use and contribute improvements to shared frameworks, and document pipelines and runbooks.
What we offer
Work:
- Flexible working hours;
- Collaborative, friendly team environment;
- Remote/Hybrid work;
Life:
- Company social events;
- Annual corporate parties;
Health:
- Comprehensive medical insurance;
Education:
- Allowances for professional education;
- English language courses with native speakers;
- Internal knowledge-sharing sessions.
What we offer
About Proxet
Proxet is a professional software development firm trusted by clients from around the world. With our expertise in AI and machine learning, we help businesses reimagine their possibilities and transform ideas into tangible digital solutions. By providing core services with an emphasis on data practices, we shape the future, one step at a time.
If you’d like to join our Proxet Nation and work closely with high-level professionals and our engineers, fill in the form!
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