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Alongside

Senior Data Engineer – Real-Time & Streaming

Alongside

. Design and deliver end-to-end streaming data products using Apache Flink, Apache Kafka, and event-driven architectures.

Posted 11/10/2026full-timePorto • PortugalSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and delivering end-to-end streaming data products using Apache Flink and Kafka, with a strong focus on building resilient data pipelines and APIs. Proficient in AWS services and CI/CD practices, ensuring high-quality, secure, and efficient data processing solutions.

Highest-signal resume keywords
Apache FlinkApache KafkaAWS ServicesREST APIsCI/CD Tooling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data EngineeringStream-Processing ApplicationsJavaPythonSQLData ModellingEvent-Driven ArchitecturesMicroservicesSchema ManagementAutomated Testing
Soft Skills
MentoringProblem-SolvingCommunication
Tools & Technologies
TerraformCloudWatchOpenTelemetryPrometheusGrafanaGitLab CIGitHub Actions
Industry Keywords
Streaming Data ProductsReal-Time SystemsData Quality ControlsObservabilitySecurity Best Practices

Tech Stack

Tools & technologies
ApacheAWSGrafanaJavaKafkaMicroservicesPrometheusPythonScalaSQLTerraform

About the role

Key responsibilities & impact
  • Design and deliver end-to-end streaming data products using Apache Flink, Apache Kafka, and event-driven architectures.
  • Develop stateful stream-processing applications, including windowing, event-time handling, watermarks, checkpointing, and exactly-once processing guarantees.
  • Build and maintain resilient ingestion, transformation, and delivery pipelines for real-time and batch workloads.
  • Manage Kafka topics, partitioning, consumer groups, schemas, and schema evolution.
  • Design and develop REST APIs, streaming interfaces, and microservices with clear contracts, versioning, authentication, and rate limiting.
  • Deploy and operate services on AWS using Infrastructure as Code with Terraform.
  • Embed data quality controls, automated testing, observability, and actionable alerting throughout the delivery lifecycle.
  • Build and improve CI/CD pipelines, including automated deployment, rollback, and safe release practices.
  • Monitor production services against agreed SLAs, investigate incidents, and optimise latency, throughput, reliability, and cost.
  • Apply security best practices across authentication, authorisation, encryption, secrets management, and least-privilege access.
  • Translate business requirements into technical designs, challenge assumptions, and propose practical solutions.
  • Review code and designs, mentor engineers, and contribute to engineering standards and technical documentation.

Requirements

What you’ll need
  • 7+ years of professional experience in data engineering, including at least 3 years building and operating real-time or streaming systems in production.
  • Strong hands-on experience with Apache Flink in production, including DataStream APIs and Flink SQL.
  • Strong Java skills, with experience in Python/PyFlink or Scala also valued.
  • Solid experience with Apache Kafka, event-driven architectures, and schema management using Avro and Schema Registry.
  • Proven experience building APIs and microservices using REST and OpenAPI.
  • Strong AWS experience, including relevant services such as MSK, Kinesis, Managed Service for Apache Flink, Lambda, API Gateway, ECS/EKS, S3, and IAM.
  • Strong SQL and data modelling skills across streaming and batch workloads.
  • Experience with CI/CD tooling such as GitLab CI or GitHub Actions, alongside automated unit, integration, contract, and load testing.
  • Experience with observability tools such as CloudWatch, OpenTelemetry, Prometheus, and Grafana.
  • Fluency in English, both written and spoken.

Benefits

Comp & perks
  • The opportunity to build production-grade real-time data products within the financial markets industry.
  • End-to-end ownership across streaming pipelines, APIs, and production operations.
  • Collaboration with platform engineers, product owners, and business stakeholders.
  • Scope to influence technical decisions, improve engineering practices, and mentor other engineers.
  • Hybrid working model in Porto.