Job
Description
- Design, develop, and operate scalable and maintainable data pipelines in the Azure Databricks environment - Develop all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation - Enable data-driven decision-making in Human Resources (HR), Purchasing (PUR) and Finance (FIN) by ensuring high data availability, quality, and reliability - Implement data products and analytical assets using software engineering principles in close alignment with business domains and functional IT - Apply rigorous software engineering practices such as modular design, test-driven development, and artifact reuse in all implementations - Global delivery footprint; cross-functional data engineering support across HR, PUR & FIN domains - Collaboration with business stakeholders, functional IT partners, product owners, architects, ML/AI engineers, and Power BI developers - Agile, product-team structure embedded in an enterprise-scale Azure environment Main Tasks: Design scalable batch and streaming pipelines in Azure Databricks using PySpark and/or Scala Implement ingestion from structured and semi-structured sources (e.g., SAP, APIs, flat files) Build bronze/silver/gold data layers following the defined lakehouse layering architecture & governance Implement use-case driven dimensional models (star/snowflake schema) tailored to HR, PUR & FIN needs Ensure compatibility with reporting tools (e.g., Power BI) via curated data marts and semantic models Implement enterprise-level data warehouse models (domain-driven 3NF models) for HR, PUR & FIN data, closely aligned with data engineers for other business domains Develop and apply master data management strategies (e.g., Slowly Changing Dimensions) Develop automated data validation tests using frameworks Monitor pipeline health, identify anomalies, and implement quality thresholds Establish data quality transparency by defining and implementing meaningful data quality rules with source system and business stakeholders and implementing related reports Develop and structure pipelines using modular, reusable code in a professional IDE Apply test-driven development (TDD) principles with automated unit, integration, and validation tests Integrate tests into CI/CD pipelines to enable fail-fast deployment strategies Commit all artifacts to version control with peer review and CI/CD integration Work closely with Product Owners to refine user stories and define acceptance criteria Translate business requirements into data contracts and technical specifications Participate in agile events such as sprint planning, reviews, and retrospectives Document pipeline logic, data contracts, and technical decisions in markdown or auto-generated docs from code Align designs with governance and metadata standards (e.g., Unity Catalog) Track lineage and audit trails through integrated tooling Profile and tune data transformation performance Reduce job execution times and optimize cluster resource usage Refactor legacy pipelines or inefficient transformations to improve scalability Degree in Computer Science, Data Engineering, Information Systems, or related discipline. Certifications in software development and data engineering (e.g., Databricks DE Associate, Azure Data Engineer, or relevant DevOps certifications). 3-6 years of hands-on experience in data engineering roles in enterprise environments. Demonstrated experience building production-grade codebases in IDEs, with test coverage and version control. Proven experience in implementing complex data pipelines and contributing to full lifecycle data projects (development to deployment) Experience in at least one business domain: HR, PUR & FIN or a comparable field Not required; however, experience mentoring junior developers or leading implementation workstreams is a plus Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines.
