Position: Engineering Manager
Working arrangement: Hybrid
Location: Global Development Hub Jakarta.
Candidates should be domicile in or willing to relocate to JABODETABEK (without relocation support).
About Us:
Byggfakta Group is a leading provider of data and insights on construction projects, serving customers worldwide. We are a global company leader in construction information and additional services. The group has a turnover of approximately €180 million and has over 1,800 employees in 26 different countries. As part of our global network, BCI Central, a key subsidiary with legal operations in Indonesia, plays a crucial role in our mission to deliver exceptional value to our clients.
About the Team:
Byggfakta Group is on an exciting growth journey where we continuously develop to remain the obvious choice for both customers and employees. Our initiative, the Global Development Hub (GDH), is envisioned to be at the forefront of technological innovation, driving the development and operation of our diverse product portfolio. In this dynamic environment, the team crafts its own processes with continuous pursuit of improvements at its foundation.
Hosted by BCI Central with legal operations in Indonesia, a seamless integration of talents and resources is ensured, thereby fostering a supportive and legally robust framework for all GDH team members.
About the Role:
As the Engineering Manager, you will oversee and guide a dynamic engineering teams specializing in ETL processes and AI/ML technologies. Your role will involve managing large-scale data processing projects, driving the implementation of AI and ML solutions, and ensuring the delivery of high-quality, scalable systems. You will collaborate with cross-functional teams to shape and execute strategic initiatives that align with our company’s goals.
Key Responsibilities:
- Lead, mentor, and inspire a team of engineers, promoting a culture of innovation and excellence in ETL and AI/ML practices.
- Oversee the end-to-end lifecycle of ETL pipelines and AI/ML projects, ensuring timely and successful delivery.
- develop and implement strategies for handling complex data processing tasks and deploying machine learning models that address key business challenges.
- Work closely with product managers, solution architect and other stakeholders to define project requirements and deliver impactful solutions.
- Work with data teams to ensure high level of data quality with efficient resource usage and cost management. Reliable, stable and efficient ETL processes are key to delivery of this product.
- Develop and lead development of automation algorithms for several automation tasks including data generation, automated data testing and monitoring of ETL pipelines identifying data level errors and unexpected values and handling.
- Ensure best practices in code quality, data integrity, and system performance. Conduct regular reviews and maintain high standards across all AI/ML and ETL processes.
- Stay abreast of emerging trends and technologies in AI/ML and ETL. Encourage continuous learning and the adoption of cutting-edge tools and methodologies.
Qualifications:
- Bachelor’s degree in Computer Science, Engineering, or a related field. Advanced degree is a plus.
- Minimum of 2-3 years of experience in data roles, with a strong background in ETL processes or AI/ML technologies.
- Proven track record of managing and mentoring data teams, with experience leading large-scale projects.
- Fluent in English, with excellent communication and presentation skills.
- Experience in Modern Python (async IO, type annotations etc.)
- Strong proficiency in Python and relevant libraries (NumPy, Pandas, PyTorch )
- Experience extracting data from PDFs (pdfminer, pdfplumber, PyMuPDF, etc.)
- NLP, text classification and/or entity extraction (using spaCy, scikit-learn, nltk, etc.)
- Experience with RAG systems and embedding models (OpenAI, Any other LLM)
- Understanding of the database ,SQLAlchemy (ideally with PostgreSQL)
Any of the following would be bonuses:
- Knowledge in ElasticSearch/Opensearch
- Consuming GraphQL
- Familiarity with AWS API (Lamda functions, S3, SQS, SNS)
