• Design, develop, and maintain data architecture using T-SQL, ETL, and Python scripting. • Automate SFTP file transfers and handle various data formats (XML, JSON, X12, Excel, Parquet) using Python. • Integrate with APIs using Python for data import/export. • Collaborate with cross-functional teams, document processes, and use Git/Github for version control. • Integrate Tableau for data representation and reporting.

Role Description - Entwickle und betreibe skalierbare Datenpipelines zwischen unseren HR-Systemen (u. a. SAP SuccessFactors) und der zukünftigen Datenplattform. - Konzipiere und implementiere moderne, performante Datenmodelle. - Bereite Daten für Dashboards und Analysen auf und stelle dabei höchste Datenqualität sowie Stabilität sicher. - Gestalte aktiv den Aufbau einer zukunftssicheren, KI-fähigen Datenplattform. Qualifications - Mehrjährige Erfahrung im Data Engineering oder Data Warehousing. - Sehr gute SQL-Kenntnisse und Erfahrung mit Cloud-Technologien. - Gutes Verständnis für Datenintegration und APIs. - Interesse an Analytics, BI, KI sowie HR-Themen. - Idealerweise Erfahrung im internationalen Konzernumfeld. Requirements - Vielfältige Mitarbeitende und Chancengleichheit. - Offenheit für Bewerbungen unabhängig von kultureller als auch sozialer Herkunft, Alter, Geschlecht, Nationalität, Religion, Behinderungen oder sexueller Orientierung. Benefits - Beratungsangebote. - Essenszuschuss. - Flexible Arbeitszeiten. - Gesundheitsangebote. - Kinderbetreuung. - Lernangebote. - Homeoffice. - Zukunftsvorsorge. Salary Für diese Position ist lt. unserem Kollektivvertrag für Telekommunikation ein monatliches Mindestgehalt von € 4.046,28,- brutto auf Vollzeitbasis (38,5 Wochenstunden) vorgesehen. Dein tatsächliches Gehalt besprechen wir noch mit dir persönlich. Wir orientieren uns dabei an deiner individuellen Qualifikation und deiner Erfahrung und bieten dir eine entsprechende, attraktive Überzahlung.

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases • Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables • Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs • Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments • Identify and implement improvements by automating manual tasks and reducing pipeline runtimes • Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

Role Description Bridgeway is seeking a Senior Data Engineer to design, develop, and maintain our data warehouse infrastructure. This role involves working closely with analysts, engineers, and other stakeholders to shape our data architecture, ensuring secure and efficient data pipelines, and enabling advanced analytics across the organization. The ideal candidate will have a strong background in data engineering, data warehousing, and ELT processes, along with a passion for optimizing data systems. This is a remote position, with preference given to East Coast candidates. - Design, develop, and maintain a scalable lakehouse architecture, including a medallion (bronze/silver/gold) data model optimized for analytics and AI/ML consumption. - Design, implement, and operate ELT pipelines, including workflow orchestration, scheduling, and monitoring, to ensure reliable and scalable execution. - Establish data quality, testing, and observability practices, and proactively monitor and resolve data and automation issues to ensure platform reliability and trust. - Ensure data security and compliance, including role-based access controls for security, encryption, masking, and governance best practices to ensure compliant handling of sensitive information. - Optimize performance of data workflows and storage for cost efficiency and speed. - Partner with engineers, analysts, and stakeholders to meet data needs; balance cost, performance, simplicity, and time-to-value while mentoring teams and documenting standards. - Provide technical leadership and mentorship to team members – guiding best practices, skill development, and collaboration cross-functionally. - Enable AI/ML use cases through well-structured data models, feature availability, and platform integrations using tools such as Databricks Vector Search and Model Serving. - Develop and maintain data pipelines using version control and CI/CD best practices in a collaborative engineering environment. - Collaborate within an Agile-Scrum framework and develop comprehensive technical design documentation to ensure efficient and successful delivery. - Serve as a trusted expert on organizational data domains, processes, and best practices. Qualifications - 5+ years of hands-on data engineering experience required. - 3+ years of experience building and operating data pipelines on a modern lakehouse platform (e.g., Databricks – Unity Catalog, Delta Live Tables, Asset Bundles), including data modeling, governance, and CI/CD deployment patterns. - 3+ years of experience with analytical SQL (ANSI SQL/T-SQL/Spark SQL) and Python for data engineering, including pipeline construction, transformation logic, and automation required. - Strong communication skills with the ability to collaborate and influence across engineering, analytics, and business stakeholders required. - Streaming and ingestion tools, such as Kafka, Kinesis, Event Hubs, Debezium, or Fivetran preferred. - DAX, LookML, dbt; Airflow/Dagster/Prefect, Terraform; Azure DevOps; Power BI/Looker/Tableau; GitHub CoPilot knowledge is a plus. - Bachelor’s degree in Computer Science, Information Technology, or a related field. Master’s degree preferred.