Senior Data Engineer at Creditstar

Tallinn, , Estonia -

Full Time

Start Date

Immediate

Expiry Date

17 Dec, 25

Salary

0.0

Posted On

18 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, SQL, Python, Cloud Platforms, AWS, Data Warehousing, Snowflake, ETL, Data Modeling, Streaming Technologies, MLOps, Mentoring, Communication, Project Management, Problem-Solving, Entrepreneurial Mindset

Industry

Financial Services

Description

Creditstar Group is a rapidly growing international consumer finance company, headquartered in Tallinn, Estonia and operating in multiple European markets. At the core, we are a financial technology company that uses automated processes, algorithms and data analysis to make financial instruments easily available to a population of more than 175 million people in our target markets. We are a team of ambitious professionals who value innovation, effective efficiency, high growth, and high performance. For more information on the group, please visit www.creditstar.com. Our vision is to build and deliver digital banking products of the future. If you want to be part of this, here is your chance. YOUR POSITION: SENIOR DATA ENGINEER YOUR ROLE: As Senior Data Engineer, you will help to shape the Creditstar’s data engineering strategy and guide the design, architecture, and implementation of scalable data solutions. You will support a team of data engineers, collaborate closely with data scientists, analysts, and business stakeholders, and ensure our data infrastructure supports advanced analytics, machine learning, and real-time decision-making. This is both a technical and leadership role- you will set standards, drive innovation, and ensure delivery of high-quality, reliable, and future-proof data systems. Experience & Expertise 6+ years of professional experience in data engineering, with experience also in a leadership role. Proven track record in architecting and scaling data solutions in fast-paced environments. Strong proficiency in SQL and Python. Hands-on expertise with cloud platforms (preferably AWS) and modern data warehousing solutions (e.g., Snowflake). Deep understanding of ETL/ELT, data modeling (dimensional & advanced techniques), and streaming technologies (Kafka, Kinesis or equivalent). Experience with MLOps practices and supporting ML model deployment and monitoring. Leadership & Collaboration Good mentoring, and team-building skills. Excellent communication skills with the ability to influence technical and business stakeholders. Strong project management and execution skills with a sense of ownership and accountability. Mindset & Approach Strategic thinker with a hands-on, problem-solving attitude. Entrepreneurial/startup mindset with the ability to thrive in a fast-scaling environment. Fluent in spoken and written English. Our Tech Stack Cloud: AWS (EC2, EMR, MSK, MWAA, EKS, RDS, etc.) Data Warehouse: Snowflake Workflow & Orchestration: Airflow, DBT Big Data & Processing: Spark, Docker, Kubernetes Streaming: Kafka, Kinesis Collaboration Tools: Atlassian, Bitbucket Nice to Have Prior experience in Financial Services or fintech. Knowledge of feature stores and advanced ML data management. Contributions to open-source data engineering projects. Dynamic, challenging, and rewarding work within a growing international company where one can REALLY make an impact; Hybrid working environment; Regular company parties and events; Brand new office in the center of the city with games and free snacks/drinks; Competitive remuneration package Sports or health insurance compensation Birthday day-off Join us and contribute to our mission while advancing your career. Apply now! * Please note that this is not a remote position and requires to be present at the office at least 3 days a week.

Responsibilities

As a Senior Data Engineer, you will shape Creditstar’s data engineering strategy and guide the design and implementation of scalable data solutions. You will support a team of data engineers and collaborate with data scientists, analysts, and business stakeholders to ensure the data infrastructure supports advanced analytics and real-time decision-making.