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Requirements:
BSc/BA in a highly quantitative field such as Industrial Engineering, Economics, Statistics, or equivalent.
5+ years of hands-on experience as a data analyst, ideally as a lead analyst within a business unit or a team lead who still loves being hands-on.
Proven experience working with cloud-based data warehouses, preferably Snowflake, with strong SQL skills.
2+ years of experience in data modeling, specifically star-schema methodologies (dimensions, facts, profiles, etc.).
2+ years of experience using Tableau.
Excellent verbal and written communication skills in English.
Bonus Points
Experience as a BI Developer.
Familiarity with predictive analytics methodologies.
Hands-on experience with Redash.
Passion for gaming and understanding of player behavior.
Background in B2C, consumer, or gaming products.
Experience working with Airflow.

Requirements:
5+ years of Data Engineering experience (preferably in a startup), with a focus on designing and implementing scalable, analytics-ready data models and cloud data warehouses (e.g., BigQuery, Snowflake).
Minimum 3 years in a leadership role, with a proven history of guiding teams to success.
Expertise in modern data orchestration and transformation frameworks (e.g., Airflow, DBT).
Deep knowledge of databases (schema design, query optimization) and familiarity with NoSQL use cases.
Solid understanding of cloud data services (e.g., AWS, GCP) and streaming platforms (e.g., Kafka, Pub/Sub).
Fluent in Python and SQL, with a backend development focus (services, APIs, CI/CD).
Excellent communication skills, capable of simplifying complex technical concepts.
Experience with, or strong interest in, leveraging AI and automation for efficiency gains.
Passionate about technology, proactively identifying and implementing tools to enhance development velocity and maintain high standards.
Adaptable and resilient in dynamic, fast-paced environments, consistently delivering results with a strong can-do attitude.
B.Sc. in Computer Science / Engineering or equivalent.

Requirements:
10+ years of professional experience in AI/ML and data science with at least 5 years in leadership roles.
Advanced degree (MSc/PhD) in Computer Science, Mathematics, or related field.
Proven success leading data science / AI teams in production environments.
Strong grounding in ML/DL, model architectures, and data pipelines.
Track record of translating research into scalable production systems with measurable business impact.
Practical trade-off mindset - build vs. buy, open-source vs. proprietary, etc.
Excellent communication skills - capable of influencing executives, mentoring engineers, and educating non-technical stakeholders.
Passion for continuous learning, innovation, and knowledge sharing.

Requirements:
1. Technical Expertise
Cloud Data Platforms: Expertise in Google Cloud Platform (GCP)
services, especially in data-related services like BigQuery, Cloud SQL,
Dataproc, Pub/Sub, and Dataflow.
Data Engineering: Strong knowledge of data pipelines, ETL/ELT
processes, and data integration using GCP tools.
Database Management: Experience with both relational and
non-relational databases, including MySQL, PostgreSQL, and NoSQL
solutions like Firestore or Bigtable.
Programming Skills: Proficiency in Python, SQL, and possibly other
languages like Java or Scala, with a focus on data manipulation and
processing.
APIs and Integrations: Experience working with Google APIs and
third-party APIs for data extraction and integration.
Machine Learning: Familiarity with Google Cloud AI tools, such as
Vertex AI, and how to integrate machine learning workflows into the data
architecture.
Data Governance: Knowledge of data security, privacy, governance, and
regulatory requirements (GDPR, HIPAA, etc.).
2. Leadership and Management
Team Management: Ability to lead a team of data engineers, analysts,
and architects, providing guidance, mentorship, and performance
management.
Project Management: Strong skills in managing data projects, ensuring
timely delivery while meeting client requirements.
Collaboration: Experience working cross-functionally with stakeholders
such as solution architects, developers, and business teams to translate
business needs into data solutions.
Resource Allocation: Efficiently managing cloud resources to optimize
cost and performance.
3. Client-Facing Skills
Consulting: Experience in providing consulting services to clients,
including understanding their data needs, providing solutions, and
guiding them through the data modernization process.
Pre-sales Support: Supporting sales teams in scoping out client
projects, preparing presentations, and explaining technical aspects of
data solutions during pre-sales.
Client Relationship Management: Building and maintaining strong
relationships with clients to ensure satisfaction and long-term
partnership.
4. Certifications and Education
Google Cloud Certifications:
Professional Data Engineer (preferred)
Professional Cloud Architect (preferred)
Other Certifications: Optional certifications in data engineering or
machine learning from organizations like Cloudera, AWS, or Microsoft
Azure can be advantageous.
Educational Background: Bachelors or Masters degree in Computer
Science, Data Science, Engineering, or a related field.
5. Soft Skills
Problem-Solving: Ability to troubleshoot data-related issues and
implement scalable solutions.
Adaptability: Ability to keep up with the rapidly changing cloud and data
landscape.
Communication: Excellent verbal and written communication skills to
explain complex data systems to non-technical stakeholders.

Requirements:
2+ years leading engineers (team lead or manager) building/operating large-scale data platforms; 5+ years total in Data Infrastructure/DataOps roles.
Proven ownership of cloud-native data platforms on AWS: S3, EMR (preferably EMR on EKS), IAM, Glue/Data Catalog, Athena.
Production experience with Apache Iceberg (schema evolution, compaction, retention, metadata ops) and columnar formats (Parquet/Avro).
Hands-on depth in at least two of: Trino/Presto, Apache Spark/Structured Streaming, Apache Druid, Apache Flink.
Strong conceptual understanding of Kubernetes (EKS), including autoscaling, isolation, quotas, and observability
Strong SQL skills and extensive experience with performance tuning, with solid proficiency in Python/Java.
Solid understanding of Kafka concepts, hands-on experience is a plus
Experience running on-call for data platforms and driving measurable SLO-based improvements.
You might also have
Experience building feature platforms (feature definitions, materialization, serving, and online/offline consistency).
Airflow (or similar) at scale; Argo experience is a plus.
Familiarity with BigQuery (and ideally BigLake/Iceberg interop) and operational DBs like Aurora MySQL.
Experience with Clickhouse / Snowflake / Databricks / Starrocks.
FinOps background (cost attribution/showback, Spot strategies).
Data quality, lineage, and cataloging practices in large orgs.
IaC (Terraform/CloudFormation).

Requirements:
At least 2 years of proven experience as a Team Lead
At least 4 years of hands-on experience as a Data or Backend Engineer, preferably in a large-scale, high-throughput production environment. Deep understanding of backend
Willingness to work in a high-pace environment: proactive, adaptable, and focused on delivering results
Ability to act under pressure and navigate complex scenarios with clear decision-making and leadership
Ability to guide and mentor engineers
Strong familiarity with agile methodologies (e.g., Scrum or Kanban), including sprint planning, retrospectives, backlog management, and continuous improvement
Excellent collaboration skills, with the ability to work cross-functionally with product managers, engineers, DevOps, and CS team
Architecture and system design, with a focus on scalability, maintainability and monitoring
Practical experience designing, developing, and maintaining distributed real-time and batch processing pipelines using tools like Kafka, Spark, Airflow and Apache Storm, along with a strong understanding of the operational aspects of running these pipelines in production
Hands-on experience working with cloud platforms (such as Azure, AWS), leveraging managed services.