Requirements:
Requirements
5+ years of hands-on experience in data engineering and in building data products for analytics and business intelligence.
Experience working on data developments using AI tools and agentic workflows.
Strong hands-on experience with ETL orchestration tools (Apache Airflow), and data lake houses (Databricks is an advantage)
Vast knowledge in both batch processing and streaming processing (e.g., Kafka, Spark Streaming).
Proficiency in Python, SQL, and cloud data engineering environments (AWS, Azure, or GCP).
Familiarity with data visualization tools (Power BI, Looker, or similar).
BSc in Computer Science or a related field from a leading university
Nice to have:
Experience working on early-stage projects, building data systems from scratch.
Background in building operational analytics pipelines, in which analytical data feeds real-time product business logic.
Experience in cost optimization in modern cloud environments.
Knowledge of data governance principles, compliance, and security best practices.

Requirements:
Requirements
5+ years of experience building and operating large-scale backend, platform, or data infrastructure systems.
Strong programming skills in Python, Go, or Node.js.
Deep understanding of distributed systems, scalability, reliability, and performance optimization.
Strong experience with relational databases such as PostgreSQL or MySQL.
Strong experience with NoSQL databases such as MongoDB, DynamoDB, Cassandra, or similar technologies.
Proven experience optimizing database performance, query execution, indexing strategies, and large-scale data models.
Experience designing and operating data lakes and large-scale storage systems.
Experience building and maintaining data pipelines and ETL/ELT workflows.
Experience with event-driven architectures and messaging platforms such as Kafka.
Hands-on experience with cloud platforms, preferably AWS.
Experience with Docker, Kubernetes, and Infrastructure as Code tools such as Terraform.
Strong experience with monitoring, observability, and production troubleshooting.
Excellent communication and collaboration skills.

Requirements:
8+ years of experience in software / data engineering, including several years in a senior / staff / architect role designing complex data systems.
Strong experience designing modern data platforms and distributed data architectures (lakehouse/warehouse, batch and streaming/event-driven patterns, robust data APIs).
Experience working with columnar/serialization data formats such as AVRO and Parquet, including schema evolution and storage trade-offs.
Experience with DBT and ELT management tools for building, testing, and maintaining transformation pipelines.
Experience with Apache Airflow (or comparable orchestration tooling) for scheduling and managing data workflows.
Experience working with Databricks (or Snowflake) and medallion architecture (bronze/silver/gold).
Experience building SaaS data infrastructure, including CI/CD, ETL/data pipeline observability, and data quality monitoring.
Proven ability to design for scale, performance, security, and reliability in production SaaS environments.
Hands-on experience with at least one major language and ecosystem used in our stack (e.g., Python, SQL, Java, or similar).
Proven agentic experience - as hands-on experience and as architecting GenAI systems.
Comfortable reading and reviewing code, guiding implementation, and occasionally building prototypes or reference implementations.
Nice to have:
Experience with financial services, B2B SaaS, or integrations with large enterprise customers (e.g., banks).
Background in analytics, BI, or ML-adjacent systems and feature/data pipelines for ML.
Familiarity with data governance, lineage, cataloging, and master data management.
Familiarity with domain-driven design, event sourcing, or CQRS patterns.
Solid understanding of cloud-native architecture (e.g., AWS/Azure/GCP), containers, and infrastructure-as-code practices.

Requirements:
Experience: 5+ years of hands-on experience designing and maintaining big data pipelines in on-premises or hybrid cloud SaaS environments.
Programming & Databases: Proficiency in one or more programming languages (Python, Scala, Java, or Go) and expertise in both SQL and NoSQL databases.
Engineering Practice: Proven experience with software engineering best practices, including testing, code reviews, design documentation, and CI/CD.
AWS Experience: Experience developing data pipelines and maintaining data lakes, specifically on AWS.
Streaming & Orchestration: Familiarity with Kafka and workflow orchestration tools like Airflow.

Requirements:
Experience: 5+ years of hands-on experience designing and maintaining big data pipelines in on-premises or hybrid cloud SaaS environments.
Programming & Databases: Proficiency in one or more programming languages (Python, Scala, Java, or Go) and expertise in both SQL and NoSQL databases.
Engineering Practice: Proven experience with software engineering best practices, including testing, code reviews, design documentation, and CI/CD.
AWS Experience: Experience developing data pipelines and maintaining data lakes, specifically on AWS.
Streaming & Orchestration: Familiarity with Kafka and workflow orchestration tools like 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:
Requirements: must have requirements: • Bachelor’s degree in Computer Science, Data Science, Statistics, or another relevant field, or equivalent professional experience. • At least 5 years of experience in Data Engineering, with a focus on Analytics and BI. • Expert-level SQL skills. • Deep experience with PostgreSQL, including performance tuning and optimization. • At least 3 years of experience with Python for data processing, automation, and testing. • At least 3 years of experience designing and implementing large-scale data models. • Experience leading performance optimization in complex data systems. • Experience establishing and implementing Data Governance and Data Quality processes. • Proven ability to translate business requirements into data architecture and technological solutions. • Experience providing technical leadership, mentoring, and leading technological initiatives. • Experience building Data and Analytics platforms from scratch. • Proven experience integrating AI and LLM tools into development and data engineering processes. Advantages: • Experience in FinTech or financial organizations. • Deep familiarity with regulation, information security, and compliance requirements. • Experience working with Data Lakes and Data Warehouses. • Experience with orchestration tools and ELT / ETL processes. • Experience working in cloud environments such as AWS, GCP, or Azure. • Familiarity with streaming technologies and real-time data processing. • Experience leading technology teams or professional excellence groups. Personality requirements: • Fast learning ability and curiosity for new technologies and methodologies. • Strategic thinking and broad system-wide perspective. • Ability to lead and influence without formal authority. • Ability to work independently and manage multiple tasks simultaneously. • High personal responsibility and ability to receive professional feedback. • Initiative, creativity, and ability to solve complex problems. • High motivation and constant drive for excellence. • Excellent interpersonal communication skills and ability to work with multiple stakeholders. • Ability to driv

Requirements:
At least 5 years of experience as a Data Engineer or Data Infrastructure Engineer
A bachelor's degree in Computer Science or a related field
Deep knowledge of databases - SQL and NoSQL
Proven experience building large-scale data infrastructures, including Change Data Capture, streaming pipelines, and customer data platforms
Hands-on experience with Python, Pulumi/Terraform, Apache Spark, Snowflake, AWS, Kubernetes, and Kafka
Familiarity with open source tools like Airflow and DBT
Familiarity with AI concepts like RAG, embeddings, and LLM context engineering - a plus
Enthusiasm about learning and adapting to the exciting world of AI - a commitment to exploring this field is a fundamental part of our culture
Ready to work in an office environment most days of the week

Requirements:
BSc/MSc in Computer Science, Engineering, or a related field.
8+ years building data or backend systems in Python or a similar coding language, with a strong focus on cloud data infrastructure and scalable systems.
Strong command of SQL and a track record of pragmatic schema design.
Deep understanding of data modelling, ETLs and streaming technologies, including hands-on experience with tools like big-data tools like AWS Kinesis / Kafka, Spark.
Familiarity with modern lakehouse / warehouse tech, like Databricks, Delta Lake, Iceberg, Snowflake, Redshift.
Strong understanding of distributed systems, microservices architecture, containerization, and CI/CD pipelines.
Proficiency in Infrastructure as Code (IaC) tools, like Terraform or AWS CDK.
Experience with containerization and orchestration tools - Docker, Kubernetes (K8s).
Ability to take full product ownership from ideation to delivery, ensuring alignment with business objectives.
Familiarity with agile development methodologies.
Excellent communication skills with the ability to work effectively across teams and a customer-oriented mindset. Clear English communication is required.
Advantages:
Deep expertise in Databricks (Delta Lake, Unity Catalog, DLT).
Knowledge of the medical tech world, including EHR (HL7, FHIR) data and imaging data.
Prior work in regulated domains (healthcare, fintech, aviation), experience with enforcing and managing compliance requirements like HIPAA or FedRAMP.

Requirements:
Qualifications & Skills:

Bachelors or masters degree in computer science, Engineering, Statistics, or a related field.

Minimum of 6 years of experience as a Data Engineer or a similar role, with a consistent record of successfully delivering ML/Data solutions.

You have built production data pipelines in the cloud, setting up data-lake and server-less solutions; ‌ you have hands-on experience with schema design and data modeling and working with ML scientists and ML engineers to provide production level ML solutions.

You have experience designing systems E2E and knowledge of basic concepts (lb, db, caching, NoSQL, etc)

Strong programming skills in languages such as Python and Java.

Experience with big data processing frameworks such, Pyspark, Apache Flink, Snowflake or similar frameworks.

Demonstrable experience with MySQL, Cassandra, DynamoDB or similar relational/NoSQL database systems.

Experience with Data Warehousing and ETL/ELT pipelines

Experience in data processing for large-scale language models like GPT, BERT, or similar architectures - an advantage.

Proficiency in data manipulation, analysis, and visualization using tools like NumPy, pandas, and matplotlib - an advantage.

Experience with experimental design, A/B testing, and evaluation metrics for ML models - an advantage.

Experience of working on products that impact a large customer base - an advantage.

Excellent communication in English; written and spoken.