Role Description The role is about: - Data Team, where you play a vital role in collecting, analyzing, and interpreting data to support decision-making across the organization. - Your tasks include data collection, ensuring quality, and developing databases. - You're skilled in SQL, Python for data manipulation and visualization tools. - Collaborative and communicative, you work with various teams to understand their needs and provide actionable insights. - Passionate about staying updated with new technologies, you drive innovation and contribute to a culture of continuous improvement. Location: remote Poland, Portugal or on-site Cyprus Reporting to: Head of Engineering What you will do: - Lakehouse Architecture and Data Platform Design - Design, build, and maintain scalable data lakehouse solutions on AWS using S3, Apache Iceberg, and AWS Glue Catalog as core platform components. - Contribute to the evolution of the medallion architecture, ensuring that bronze, silver, and gold layers are reliable, performant, and aligned with business needs. - CDC and Streaming Data Ingestion - Build and support real-time and near-real-time ingestion pipelines that stream operational data from on-premise databases into AWS using Debezium, Kafka, Kafka Connect, and Iceberg sinks. - Monitor and troubleshoot streaming pipelines, including connector issues, schema changes, data consistency, and ingestion reliability. - Data Modelling and Business-Ready Layers - Design and implement silver and gold layer datasets that transform raw operational data into trusted, business-ready data products. - Work closely with analysts, analytics engineers, and business stakeholders to understand requirements and create reusable, well-structured data models. - Batch and Distributed Processing - Develop and maintain batch and distributed processing jobs using PySpark on AWS EMR and AWS Glue. - Optimize data transformation jobs for performance, scalability, reliability, and cost efficiency. - Data Integration and Orchestration - Build and maintain data workflows using Apache Airflow for API ingestion, batch processing, and orchestration across the medallion architecture. - Support integrations from external and third-party systems using tools such as Airbyte. - Data Quality, Governance, and Reliability - Implement data quality checks, validation processes, and reconciliation logic to ensure trusted and consistent datasets across the platform. - Contribute to data governance practices around documentation, ownership, lineage, access, and compliance. - Infrastructure and DevOps Collaboration - Collaborate on AWS infrastructure and deployment practices, working with services such as S3, Glue, EMR, IAM, EKS, and related platform components. - Support infrastructure-as-code workflows using Terraform and Terragrunt in collaboration with engineering and infrastructure teams. Qualifications - Minimum 3+ years of experience in data engineering or related roles. - Strong experience with SQL and data modeling for analytics and reporting use cases. - Strong programming experience with Python, ideally including PySpark. - Experience designing and maintaining ETL/ELT pipelines in production environments. - Experience with real-time or near-real-time data ingestion. - Experience working with Kafka or similar streaming technologies. - Experience with CDC concepts and tools such as Debezium. - Experience with data lake or lakehouse architectures on cloud platforms. - Hands-on experience with AWS data services such as: S3, Glue Catalog, Glue Jobs, EMR, IAM, Related AWS services. - Experience with Apache Iceberg, Delta Lake, or similar open table formats. - Experience designing curated analytics layers, such as silver and gold layers in a medallion architecture. - Experience with orchestration tools such as Apache Airflow or Dagster. - Experience working with relational databases such as MySQL, MariaDB, or PostgreSQL. - Working knowledge of Unix/Linux environments and shell scripting. - Understanding of data quality, governance, lineage, and production monitoring concepts. Good to Have - Experience with Apache Druid, ClickHouse, Snowflake, Databricks, or other analytical databases and warehouse platforms. - Experience with Airbyte or similar data integration tools. - Experience with dbt or collaboration with analytics engineering teams. - Experience with Terraform and Terragrunt. - Experience with Docker and Kubernetes. - Experience optimizing Spark jobs on EMR or AWS Glue. - Experience with data visualization tools such as: Tableau, Power BI, Superset, AWS QuickSight. - Experience with data observability, alerting, and monitoring tools. Benefits - Future-Proof Your Finances: Once you’ve passed probation, we’ll kickstart your Provident Fund to secure your future. - Grow with Us: Annual Learning Budget for professional development (eligible after probation)—because your growth is our growth. - Wolt Your Way Through Lunch: €150 monthly Wolt allowance to keep you fueled and happy. - Stay Active Your Way: Enjoy a SportsBenefits membership giving you access to a wide variety of gyms and sports facilities to support your active lifestyle. - Drive in Style: After one year with us, you may be eligible for a company car—performance and availability permitting. - Park with Ease: Complimentary parking space just steps from the office, so your commute is as smooth as your workday. - Max Out Your Downtime: 25 days of vacation + public holidays, plus an additional 10 sick days. - Shop & Save: Exclusive local discount card + tickets for exciting events like Beonix, basketball games, and more. - Speak Like a Local: Join free Greek language classes, twice a week, open to all team members. - Celebrate Together: We bring colleagues from all offices together for unforgettable company celebrations. - Global Collaboration & Events: Opportunities to participate in international company events and initiatives, connecting with colleagues from all regions and contributing to a truly global community. Hiring Process - Step 1 – Thinking in Action (45 minutes): Your first conversation will be with our Talent Acquisition team. We'll explore your background, career journey, motivations, and overall fit for the role. As part of this discussion, you'll also complete a short technical screening that will be reviewed by our engineering team. - Step 2 – Build in Production (60–90 minutes): Meet with the Hiring manager and technical experts for a practical assessment. Depending on the role, this may involve a live coding exercise or a real-world scenario designed to evaluate your technical skills, problem-solving approach, and ability to work through challenges similar to those faced by our teams. - Step 3 – Final Interview (45 minutes): The final stage is a group interview with senior members of our Technology squad, which may include the CTO and Head of Engineering. Together, we'll discuss team fit, collaboration style, expectations from both sides, and any remaining questions about the role, team, or technology domain. Let's embark on a journey to redefine the landscape of payments together. We're not just offering a role; we're inviting you to be a part of something bigger. Join our team, and let's innovate, disrupt, and lead the future of payments. Together, we can make an impact that resonates. Welcome to the team! Please review our Privacy Policy to understand how we process your personal data during the recruitment process: https://payabl.com/privacy-policy

Role Description As our Senior Data Engineer, you will focus on the acquisition of data, the flow of data through our data lakehouse to our data consumers. You will deliver data products to internal company employees as well as build data products for our external customers. You will help design and implement these systems and will interact with teams on both ends of the data flow. - Define and implement data pipelines using AWS, Fivetran, Snowflake, dbt, and Looker. - Serve as a key technical authority and team influencer, driving the vision, delivery, and continuous improvement of data products. - Translate business strategy into practical data engineering execution. - Lead the team in adopting and utilizing tools such as Fivetran, dbt, Iceberg, Dataiku, and feature flag frameworks. - Capture all important business data from our transactional systems into our data lakehouse. - Act as a crucial link between data engineering, general engineering, data science, and product teams during planning and delivery. - Emphasize clear communication, comprehensive documentation, and empower the team to deliver effectively. - Architect and lead the adoption of data technologies, integrating tools like Fivetran and dbt. - Support IAC strategy for infrastructure and pipeline deployment, ensuring compliance, rollback safety, and peer-reviewed workflows. - Identify best practices for feature flag toggling strategies in critical data pipelines. - Drive internal knowledge sharing and technical writing culture, including mentoring junior staff on documentation standards. - Thrive in collaborative team environments and mentor junior team members by providing guidance, support, and best practices. - Adept at designing scalable architectural solutions while effectively communicating complex concepts to both technical and non-technical audiences. Qualifications - At least 6 years experience in data engineering. - Strong understanding of ETL and data pipeline tools and techniques. - Proficiency in at least one SQL variant (we use MySQL and SnowSQL), and writing performant queries. - Highly collaborative team player with team leadership skills. - Experience with dbt or similar tools for moving, storing, and transforming data. - Strong programming experience, preferably in Python, or other object-oriented language. - Build highly reliable systems. - Strong diagnostic skills. - Strong organizational and communication skills. Requirements - The annual base salary range for this role is $150,000 — $185,000 USD. - Actual compensation may vary based on factors including the candidate's experience, qualifications, skills, and work location. - This position will remain posted until filled. Benefits - Competitive salary and benefits with growth-company options grant. - Fast-paced and professional work culture. - Stock options with standard startup vesting - 1 year cliff; 4 years total. - $50 monthly communication expense stipend to go towards your phone/internet bill. - $250 stipend to enhance your WFH setup. - Reimbursement for peripheral equipment: monitor (up to $400), keyboard and mouse (up to $200). - Premium medical benefits including vision and dental (100% coverage for employees). - Company-sponsored life and disability insurance. - Paid parental bonding leave. - Paid sick leave, jury duty, bereavement. - 401k plan. - Flexible Time Off (our team members typically take off ~3-4 weeks per year). - Volunteer Time Off. - 13 scheduled holidays.

Role Description DecisionPoint seeks a Data Engineer to design, develop, and maintain secure data pipelines, transformations, and analytics infrastructure supporting enterprise systems within a Department of Defense (DoD) modernization initiative. The Data Engineer will ensure accurate, reliable, and performant data flow across applications, integrating structured and unstructured data from multiple mission sources into cloud-based storage and analytics environments. This position plays a critical role in enabling interoperability, analytics, and automation across AWS GovCloud IL2–IL5 environments through the use of scalable data engineering practices and secure DevSecOps integration. This position is fully remote. The Data Engineer will: - Design, implement, and maintain data pipelines and ETL processes supporting ingestion, transformation, and validation of mission data. - Develop and optimize data models and schemas across relational and non-relational databases to support system integrations and analytics. - Collaborate with system architects, integration developers, and data analysts to ensure data consistency, security, and integrity across cloud environments. - Implement data migration and synchronization between legacy systems, applications, and modern cloud platforms. - Utilize AWS services (Glue, Lambda, S3, RDS, Redshift, Kinesis) to build and sustain scalable and fault-tolerant data infrastructure. - Support data validation and reconciliation, performing quality checks and developing reports to ensure accuracy. - Integrate data from APIs, streaming sources, and file-based systems into centralized repositories or data lakes. - Automate data workflows using infrastructure-as-code and CI/CD principles to ensure repeatability and efficiency. - Monitor and troubleshoot data pipeline performance, ensuring adherence to SLAs and operational reliability. - Implement data encryption, masking, and access controls in compliance with DoD cybersecurity policies and RMF requirements. - Support development of dashboards and analytics products, enabling data-driven insights for mission stakeholders. - Maintain documentation and metadata repositories, including data dictionaries, lineage, and technical specifications. - Participate in Agile sprints, contributing to backlog refinement, testing, and cross-functional collaboration. Qualifications - Clearance Requirement: Must hold an active Secret clearance. - Education: Bachelor’s degree in Computer Science, Information Systems, or a related technical field. - Experience: Minimum 7 years of experience in data engineering, integration, or analytics enablement. - Experience building and optimizing data pipelines in enterprise or federal environments. - Familiarity with Agile software development and DevSecOps delivery frameworks. - Related technical certification required. Requirements - Proficiency with Python, SQL, and ETL frameworks (e.g., Apache NiFi, Talend, or AWS Glue). - Experience with cloud data services, preferably AWS GovCloud (RDS, S3, Lambda, Glue, Redshift). - Knowledge of database management systems (PostgreSQL, SQL Server, DynamoDB, MongoDB). - Familiarity with RESTful API integration and data exchange standards (JSON, XML, CSV). - Understanding of DoD data security and compliance standards, including encryption, RMF, and STIG adherence. - Experience developing and maintaining data validation, profiling, and transformation scripts. - Exposure to data visualization and analytics tools (Power BI, Tableau, QuickSight). Certifications (Preferred) - AWS Certified Data Engineer – Associate or AWS Certified Developer. - CompTIA Security+ CE or equivalent DoD 8570 certification. - Certified Data Management Professional (CDMP) or equivalent. Skills - Strong analytical and problem-solving skills for data integration and performance tuning. - Excellent communication and collaboration across technical and functional teams. - Attention to detail with focus on data accuracy and consistency. - Ability to balance data governance, security, and performance objectives. - Commitment to continuous improvement and mission impact through data-driven decision-making.

Role Description - Diseñar, desarrollar y mantener soluciones de ingeniería de datos sobre ecosistema Microsoft Azure. - Diseñar y construir modelos de datos optimizados para entornos analíticos y grandes volúmenes de información. - Desarrollar pipelines de integración y transformación de datos (ETL/ELT) utilizando Azure Databricks y Azure Data Factory. - Crear y optimizar consultas SQL, procedimientos almacenados y estructuras de bases de datos. - Participar en el diseño de arquitecturas de datos escalables y eficientes. - Implementar procesos de carga, transformación y explotación de datos para diferentes áreas de negocio. - Garantizar la calidad, integridad y disponibilidad de la información. - Colaborar con equipos multidisciplinares en entornos Agile. - Participar en iniciativas de mejora continua y automatización de procesos de datos. - Documentar desarrollos, procesos y soluciones implementadas. Qualifications - Experiencia mínima de 4 años como Data Engineer. - Experiencia demostrable en Azure Databricks, Azure Synapse Analytics, Azure Data Factory y Azure SQL Database. - Experiencia sólida en diseño y construcción de modelos de datos. - Experiencia en definición de tablas, relaciones complejas, optimización de consultas y procedimientos almacenados. - Experiencia en desarrollo de pipelines y transformaciones de datos complejas en Databricks. - Nivel avanzado de SQL. - Experiencia con Python para procesamiento y transformación de datos. - Experiencia trabajando en proyectos bajo metodologías Agile. - Nivel de inglés mínimo B2. - Capacidad para trabajar de forma autónoma en entornos complejos de ingeniería de datos.