Role Description Lead data engineers at Thoughtworks develop modern data architecture approaches to meet key business objectives and provide end-to-end data solutions. They might spend a few weeks with a new client on a deep technical review or a complete organizational review, helping them to understand the potential that data brings to solve their most pressing problems. On projects, they will be leading the design of technical solutions, or perhaps overseeing a program inception to build a new product. Alongside hands-on coding, they are leading the team to implement the solution. - You will lead and manage data engineering projects from inception to completion, including goal-setting, scope definition and ensuring on-time delivery with cross team collaboration. - You will collaborate with stakeholders to understand their strategic objectives and identify opportunities to leverage data and data quality. - You will design, develop and operate modern data architecture approaches to meet key business objectives and provide end-to-end data solutions. - You will be responsible to create, design and develop intricate data processing pipelines, addressing clients' most challenging problems. - You will collaborate with data scientists to design scalable implementations of their models. - You write clean and iterative code based on TDD and leverage various continuous delivery practices to deploy, support and operate data pipelines. - You will lead and advise clients on how to use different distributed storage and computing technologies from the plethora of options available. - You will develop data models by selecting from a variety of modeling techniques and implementing the chosen data model using the appropriate technology stack. - You will be responsible for data governance, data security and data privacy to support business and compliance requirements. - You will define the strategy for and incorporate data quality into your day-to-day work. Qualifications - You have experience with the Databricks platform. - You have experience in Gen/AI and LLMs. - You have experience in leading the system design and implementation of technical solutions. - Working with data excites you; You have created Big Data architecture, can build and operate data pipelines, and maintain data storage, all within distributed systems. - You have a deep understanding of data modeling and experience with modern data engineering tools and platforms. - You have experience in writing clean, high-quality code using the preferred programming language. - You have built and deployed large-scale data pipelines and data-centric applications using any of the distributed storage platforms and distributed processing platforms in a production setting. - You have experience with data visualization techniques and can communicate the insights as per the audience. - You have experience with data-driven approaches and can apply data security and privacy strategy to solve business problems. - You have experience with different types of databases (i.e.: SQL, NoSQL, data lake, data schemas, etc.). Requirements - You have advanced English. - You understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way. - You are resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives. - You don’t shy away from risks or conflicts, instead you take them on and skillfully manage them. - You coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work. - You enjoy influencing others and always advocate for technical excellence while being open to change when needed. - You are a proven leader with a track record of encouraging teammates in their professional development and relationships. - Cultivating strong partnerships comes naturally to you; You understand the importance of relationship building and how it can bring new opportunities to our business. Benefits - There is no one-size-fits-all career path at Thoughtworks: however you want to develop your career is entirely up to you. - Your career is supported by interactive tools, numerous development programs and teammates who want to help you grow. - We see value in helping each other be our best and that extends to empowering our employees in their career journeys. Company Description Thoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. Together, let’s be extraordinary.

• Own Workday integrations end-to-end, driving reliable data flow and scalable system design • Elevate architecture standards while bridging technical and business stakeholders • Enable high-quality user experiences through automated testing, reporting, and AI-driven insights • Design, build, test, and maintain Workday integrations using tools such as EIB, Core Connectors, Workday Studio, and APIs • Manage integrations across payroll, benefits, identity management, finance systems, data warehouses, and other HR platforms • Ensure data integrity through transformation logic, mapping, and bulk data operations • Monitor integrations, troubleshoot failures, and resolve data discrepancies • Develop and maintain reporting, data feeds, and pipelines to support HR, Finance, and business intelligence platforms • Proactively identify issues using logs, reporting, and automated alerts • Act as a bar raiser for integration and data architecture, promoting best practices in scalability, performance, cost efficiency, and user experience • Drive architectural decisions that balance cost, performance, and end-user experience • Identify and implement automation and AI-enabled solutions to improve data quality, anomaly detection, and operational efficiency • Partner with engineering and data teams to support AI-ready data pipelines • Support cross-functional testing and validation efforts through automated reporting, data checks, and failure detection • Partner with functional teams (e.g., Talent, Payroll, Benefits) to enable effective test plans and issue identification • Contribute to a high-quality user experience by ensuring data accuracy and system reliability across workflows • Partner with Core HCM architects and business process administrators across SKUs, including Talent and Advanced Compensation, to ensure cohesive, end-to-end system design • Translate complex technical concepts into clear insights for non-technical audiences • Collaborate across HR, IT, Finance, and vendors to align solutions with business needs • Ensure integrations meet security, privacy, and compliance standards • Manage secure data transfers and authentication methods across systems

**Responsibilities** The Data Engineer has the following areas of work and responsibilities: **Data Engineering** - Write Python scripts to acquire data and process data from various academic, government, or commercial sources for use in Meridia’s product development - Maintain accurate, high-quality metadata on the company’s internal datasets using scripts, automation, and selected tooling - Develop internal tools and scripts to automate data processing and use technologies like Airflow, AWS, Google Cloud to scale and optimise ETL tasks of geospatial data - Maintain the team’s git repository of ETL scripts and adhere to version control best practices to ensure code is well organized, accessible, and reproducible by the entire team - Optimize data ingestion processes to ensure reliable, timely, and scalable delivery of data into downstream products and analytics workflows, particularly for large-scale raster and vector datasets - Monitor ingestion pipelines with logging, alerting, and metrics to detect failures or data inconsistencies - Contribute to our (internal) data catalogues, manuals, dictionaries, glossary and broader set of terminology in a coherent way across countries, products and projects. - Drive continuous improvements in operational efficiency by identifying bottlenecks and implementing solutions **Core Data Engineering Skills** - Expertise in building, optimizing, and maintaining scalable ETL/data pipelines, especially for large and complex geospatial datasets, using modern orchestration and workflow tools such as Apache Airflow. - Proficient in cloud platforms (e.g. AWS), with hands-on experience architecting data storage, transformation, and metadata solutions in a secure and cost-effective manner using Infrastructure as Code tools such as Terraform - Skilled in Python and relevant data engineering frameworks (e.g., Pandas, Pydantic, Rasterio, Tippecano) for automating data ingestion, cleaning, standardization, and geospatial transformation. - Solid understanding of data governance and best practices in version control (e.g., Git), documentation, data quality assurance, reproducibility, and collaboration in cross-functional data teams. **Learning and Development** The ideal candidate should be eager and willing to learn and grow in the role. You will be engaged in on-the-job training and development to learn about the land rights sector and our data systems and workflows. You will be mentored by our Data Engineering team in Indonesia and senior colleagues in the Netherlands.

• Deliver end-to-end HR data migration activities for the SuccessFactors implementation. • Design, develop, and execute data extraction, transformation, and loading (ETL) processes. • Support payroll parallel testing by preparing and loading necessary data sets. • Manage and execute delta loads to ensure data synchronisation and accuracy. • Perform data cleansing, validation, and reconciliation activities. • Collaborate with functional consultants and business users to understand data requirements and resolve discrepancies. • Document data migration processes, mappings, and validation rules.