Data Scientist at Kpler enhancing Gas and Power teams to aggregate data for future forecasts. Collaborating with engineers and product teams for model deployment and performance enhancement.
About the role
- Leading data architecture and executing AI strategy for digital asset integration at QCP. Building a high-performing data team and ensuring compliance and operational excellence.
Responsibilities
- Drive and execute the company’s end-to-end data architecture and AI strategy, aligned with business and regulatory requirements.
- Build and lead a high-performing data engineering, analytics, and AI/ML team from the ground up.
- Architect and implement cloud-native data infrastructure on AWS, including data lakes, warehouses, pipelines, and model deployment workflows.
- Establish data governance, data quality, and security standards to ensure compliance and operational excellence.
- Collaborate and build alongside product, engineering, risk, and compliance teams to deliver scalable data solutions and AI-driven insights.
- Drive adoption of advanced analytics, ML models, and automation to improve decision-making and customer experience.
- Lead the design and maintenance of data pipelines using common industry tools such as Apache Airflow, AWS Step Functions, AWS Glue, dbt, Apache Spark/EMR, Kafka/Kinesis, and modern ETL/ELT platforms (e.g., Fivetran, Stitch).
- Oversee data extraction, cleaning, transformation, cataloging, and loading using tools such as AWS Glue DataBrew, AWS Lake Formation, AWS Glue Catalog, Delta Lake, Great Expectations, and Snowflake/Redshift utilities.
- Communicate strategies, progress, and outcomes clearly to executive stakeholders and the broader organization.
- Provide thought leadership on industry trends, AI capabilities, crypto-specific data needs, and best-practice engineering patterns.
- Mentor and develop team members, fostering a culture of technical excellence, accountability, and continuous improvement.
Requirements
- Strong communication skills with the ability to translate complex technical concepts to non-technical stakeholders.
- Proven experience in building, leading, and managing high-performing data and engineering teams.
- Minimum 10 years of relevant experience in data engineering, AI/ML engineering, data architecture, or related fields.
- Strong expertise in data and systems engineering, with hands-on experience across data platform design and build, data modelling, and large-scale distributed systems.
- Deep familiarity with AWS cloud services (e.g., S3, Glue, EMR, Redshift, Lambda, Step Functions, Lake Formation).
- Proficiency in common data ingestion, transformation, and orchestration tools such as Kafka, Kinesis, Apache Beam, Airflow, Spark, Snowflake, or Databricks.
- Proactive, resourceful, and solution-oriented mindset with strong problem-solving capabilities.
- Positive attitude, willingness to learn, and adaptability in a fast-paced environment.
- Experience working within regulated or high-integrity environments (finance, crypto, fintech) is a plus.
Benefits
- **The Environment We Offer**
- As a growing firm with a tightly-knit team, we respect and listen to all our employees. You will get the chance to make an impact by having your voice heard by everyone, including the management.
- Our employees enjoy a high level of autonomy at work. We focus on substance, not form - as long as you can perform, you will be recognized and rewarded. We are also dedicated to supporting our staff and ensuring they develop holistically to maximize their potential in the long- term.
- We also provide flexible working arrangement as required and a casual and fun work environment to boot!
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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