Data Engineer

payabl.; United States; Remote; USD 100,000 - 140,000; Full time

Job summary

payabl. is expanding its Data Team to build and maintain reliable data pipelines and datasets that empower analytics across the organization. You will work with SQL, Python, cloud data platforms, batch and streaming pipelines, and modern data engineering technologies.

Location options include Remote – Poland or Portugal, or On-site – Cyprus. Reporting to Head of Engineering, you will collaborate with engineers, analysts, and business teams to improve data quality and usability.

Qualifications

• 3+ years of experience in data engineering or related role.
• Proficient in Python and SQL.
• Experience building or maintaining ETL/ELT pipelines.
• Experience with relational and non-relational databases (MySQL, MariaDB, PostgreSQL, MongoDB).
• Familiarity with cloud data platforms, preferably AWS.
• Understanding of data engineering concepts like incremental processing, data quality, schema changes, and pipeline reliability.
• Comfortable with Git and Unix/Linux environments.
• Strong problem-solving and debugging skills.
• Willingness to learn new technologies across data platform components.

Responsibilities

• Build and maintain reliable ETL/ELT data pipelines using SQL and Python.
• Develop and maintain datasets used for analytics, reporting, and business use cases.
• Work with batch and streaming data pipelines on AWS.
• Contribute to ongoing improvements of the data platform, architecture, and practices.
• Collaborate with analysts, engineers, and business teams to translate data requirements into solutions.
• Create well-structured, reusable datasets for company-wide consumption.
• Support data transformations and data modelling for analytics and reporting.

Skills

Python

SQL

ETL/ELT pipelines

AWS

Git

Unix/Linux

Problem solving

Learning mindset

Tools

Spark/PySpark

Airflow

Dagster

DBT

Kafka

Airbyte

Terraform

Docker

Kubernetes

Job description

payabl. empowers businesses to grow through payments innovation and banking services. Our ambition is to expand our strong portfolio of global financial services we provide to businesses and make them all available in one place on our platform we call payabl.one. As a licensed financial company with principal membership with card schemes, we specialize in global payments and providing businesses with multi-currency accounts.

The role is about:

Working as part of our Data Team to build and maintain reliable data pipelines and datasets that support analytics and decision-making across the organization. You will work with SQL, Python, cloud data platforms, batch and streaming pipelines, and modern data engineering technologies.

You will collaborate with engineers, analysts, and business teams to understand data requirements, improve data quality, and make data easier to consume. We are looking for someone with strong data engineering fundamentals who is curious, enjoys solving problems, and is interested in developing their skills across modern data platform technologies.

Location: Remote – Poland or Portugal | On-site – Cyprus

Reporting to: Head of Engineering

What you will do:

Data Pipelines and Data Platform

• Build and maintain reliable ETL/ELT data pipelines using SQL and Python.
• Develop and maintain datasets used for analytics, reporting, and other business use cases.
• Work with batch and streaming data pipelines running on AWS.
• Contribute to the continuous improvement of our data platform, architecture, and engineering practices.

Data Modelling and Business-Ready Data

• Work with analysts, engineers, and business teams to understand data requirements and translate them into technical solutions.
• Develop well-structured and reusable datasets that make data easier to consume across the organization.
• Support data transformations and data modelling for analytics and reporting use cases.
• Contribute to improving the consistency, usability, and reliability of business data.

Data Quality and Production Reliability

• Support data quality checks and validation processes to ensure reliable and consistent datasets.
• Monitor data pipelines and help identify and troubleshoot data or pipeline issues.
• Support production reliability and investigate issues when pipelines or datasets do not behave as expected.
• Contribute to improvements in monitoring, observability, documentation, and data reliability practices.

Batch and Streaming Processing

• Work with batch and streaming data pipelines across the data platform.
• Support data processing and transformation workflows running on cloud-based infrastructure.
• Contribute to the development and maintenance of scalable and reliable data processing solutions.

Data Integration and Platform Development

• Work with data from relational and non-relational databases and other internal or external data sources.
• Support integrations and data ingestion workflows across different systems.
• Contribute to the evolution of the data platform as new requirements and technologies are introduced.

What we need:

• 3+ years of experience in data engineering, software engineering, analytics engineering, or a related role.
• Good programming skills in Python and SQL.
• Experience building or maintaining ETL/ELT pipelines.
• Experience working with relational and non-relational databases such as MySQL, MariaDB, PostgreSQL, MongoDB or similar.
• Familiarity with cloud-based data platforms, preferably AWS.
• Understanding of basic data engineering concepts such as incremental processing, data quality, schema changes, and pipeline reliability.
• Comfortable working with Git and Unix/Linux environments.
• Good problem-solving and debugging skills.
• Willingness to learn new technologies and work across different parts of the data platform.

Good to Have

You do not need experience with all of the technologies below. Experience with one or more would be beneficial:

Data Processing and Cloud

• You do not need experience with all of the technologies below. Experience with one or more would be beneficial:
• Apache Spark or PySpark.
• AWS services such as S3, Glue, EMR, IAM, or Athena.
• Data lake or lakehouse architectures.
• Apache Iceberg, Delta Lake, Hudi, or another open table format.

Streaming and Data Integration

• Apache Kafka or another streaming platform.
• Change Data Capture concepts or tools such as Debezium.
• Airbyte or other data integration platforms.

Orchestration and Data Architecture

• Apache Airflow, Dagster, or another workflow orchestration tool.
• Medallion architecture concepts such as bronze, silver, and gold data layers.
• DBT or experience working with analytics engineering teams.

Infrastructure and Engineering

• Terraform, Terragrunt, or other infrastructure-as-code tooling.
• Docker or Kubernetes.
• Data quality, observability, monitoring, lineage, or governance tooling. …