AWS Data Architect: Scalable Pipelines & Streaming

SoftServe; Poland; On-site; PLN 180,000 - 270,000; Full time

Job summary

SoftServe is seeking a data engineering specialist to design and implement scalable data platforms in AWS. You will work on batch and streaming pipelines, collaborating with technical and business stakeholders to deliver end-to-end solutions.

You will leverage Python/SQL, Spark, and modern data warehousing tools to build robust data architectures, while contributing to architecture discussions and knowledge sharing within the team.

Qualifications

• Proven experience designing scalable data platforms and data pipeline architectures.
• Hands-on experience with batch and streaming data processing solutions.
• Strong expertise in AWS cloud services for data engineering and analytics workloads.
• Proficiency in Python, SQL, and at least one JVM language (Java/Scala).
• Knowledge of big data technologies such as Spark, Databricks, or Flink.
• Experience with orchestration tools (Airflow/MWAA).
• Experience with streaming (Kafka/MSK/Kinesis) and data warehousing (Snowflake/Redshift).
• Upper-intermediate or higher level of English.

Responsibilities

• Design and implement scalable data pipelines using AWS services.
• Develop batch and streaming data processing architectures.
• Collaborate with stakeholders to translate requirements into technical solutions and plans.
• Support data ingestion and source-to-target mappings for new data sources.
• Participate in architecture discussions to scale the data platform and data models.
• Work with Airflow/MWAA to manage workflows.
• Support real-time processing with Kafka, MSK, or Kinesis.

Skills

AWS

Python

SQL

Data Engineering

Batch & streaming

Stakeholder communication

Tools

Airflow

MWAA

Kafka

MSK

Kinesis

Snowflake

Redshift

Databricks

Spark

Flink

Job description

SoftServe is seeking a data engineering specialist to design and implement scalable data platforms in AWS. You will work on batch and streaming pipelines, collaborating with technical and business stakeholders to deliver end-to-end solutions.

You will leverage Python/SQL, Spark, and modern data warehousing tools to build robust data architectures, while contributing to architecture discussions and knowledge sharing within the team.