דרושים » דאטה » Senior data Engineer - Fintech Product Company

we are seeking a Senior Data Infra Engineer. You will be responsible for designing and building all data, ML pipelines, data tools, and cloud infrastructure required to transform massive, fragmented data into a format that supports processes and standards. Your work directly empowers business stakeholders to gain comprehensive visibility, automate key processes, and drive strategic impact across the company.

Responsibilities

Design and Build Data Infrastructure: Design, plan, and build all aspects of the platform's data, ML pipelines, and supporting infrastructure.
Optimize Cloud Data Lake: Build and optimize an AWS-based Data Lake using cloud architecture best practices for partitioning, metadata management, and security to support enterprise-scale operations.
Lead Project Delivery: Lead end-to-end data projects from initial infrastructure design through to production monitoring and optimization.
Solve Integration Challenges: Implement optimal ETL/ELT patterns and query techniques to solve challenging data integration problems sourced from structured and unstructured data.

In your role as a Data Engineer , you'll be actively engaged in the most fascinating and demanding projects for our clients. Handling high-scale operations, heavy traffic, stringent SLAs, and massive data volumes is just the starting point. Our leadership in the realms of Big Data, Streaming Data, and Complex Event Processing (CEP) stems from our adept use of cutting-edge, optimal technologies.
Responsibilities:
Apply software-engineering methods and best practices (Medallion Architecture) in Data Lake management, using tools like DBT.
Develop Data pipelines to ingest and transform data.
Manage and enhance orchestrated pipelines across company
Data Modeling: Developing data models to ensure efficient storage, retrieval, and processing of data, often involving schema design for NoSQL databases, data lakes, and data warehouses. This will also include catalogs of data.
Scalability: Ensuring that the big data architecture can scale horizontally to handle large volumes of data and high traffic loads.
Data Security: Implementing security measures to protect sensitive data, including encryption, access controls, and data masking.
Data Governance: Establishing data governance policies to ensure data quality, compliance with regulations, and data lineage.

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from an office.

We are looking for a talented Data Engineer to help build and enhance the data platform that supports analytics, operations, and data-driven decision-making across the organization. You will work hands-on to develop scalable data pipelines, improve data models, ensure data quality, and contribute to the continuous evolution of our modern data ecosystem.

Youll collaborate closely with Senior Engineers, Analysts, Data Scientists, and stakeholders across the business to deliver reliable, well-structured, and well-governed data solutions.


What Youll Do:

Engineering & Delivery

Build, maintain, and optimize data pipelines for batch and streaming workloads.

Develop reliable data models and transformations to support analytics, reporting, and operational use cases.

Integrate new data sources, APIs, and event streams into the platform.

Implement data quality checks, testing, documentation, and monitoring.

Write clean, performant SQL and Python code.

Contribute to improving performance, scalability, and cost-efficiency across the data platform.

Collaboration & Teamwork

Work closely with senior engineers to implement architectural patterns and best practices.

Collaborate with analysts and data scientists to translate requirements into technical solutions.

Participate in code reviews, design discussions, and continuous improvement initiatives.

Help maintain clear documentation of data flows, models, and processes.

Platform & Process

Support the adoption and roll-out of new data tools, standards, and workflows.

Contribute to DataOps processes such as CI/CD, testing, and automation.

Assist in monitoring pipeline health and resolving data-related issues.

We are seeking a Senior Backend & Data Engineer to join its SaaS Data Platform team.
This role offers a unique opportunity to design and build large-scale, high-performance data platforms and backend services that power our cloud-based products.
You will own features end to end-from architecture and design through development and production deployment-while working closely with Data Science, Machine Learning, DevOps, and Product teams.
What Youll Do:
Design, develop, and maintain scalable, secure data platforms and backend services on AWS.
Build batch and streaming ETL/ELT pipelines using Spark, Glue, Athena, Iceberg, Lambda, and EKS.
Develop backend components and data-processing workflows in a cloud-native environment.
Optimize performance, reliability, and observability of data pipelines and backend services.
Collaborate with ML, backend, DevOps, and product teams to deliver data-powered solutions.
Drive best practices, code quality, and technical excellence within the team.
Ensure security, compliance, and auditability using AWS best practices (IAM, encryption, auditing).
Tech Stack:
AWS Services: S3, Lambda, Glue, Step Functions, Kinesis, Athena, EMR, Airflow, Iceberg, EKS, SNS/SQS, EventBridge
Languages: Python (Node.js/TypeScript a plus)
Data & Processing: batch & streaming pipelines, distributed computing, serverless architectures, big data workflows
Tooling: CI/CD, GitHub, IaC (Terraform/CDK/SAM), containerized environments, Kubernetes
Observability: CloudWatch, Splunk, Grafana, Datadog
Key Responsibilities:
Design, develop, and maintain scalable, secure backend services and data platforms on AWS
Build and operate batch and streaming ETL/ELT pipelines using Spark, Glue, Athena, Iceberg, Lambda, and EKS
Develop backend components and data processing workflows in a cloud-native environment
Optimize performance, reliability, and observability of data pipelines and backend services
Collaborate with ML, backend, DevOps, and product teams to deliver data-driven solutions
Lead best practices in code quality, architecture, and technical excellence
Ensure security, compliance, and auditability using AWS best practices (IAM, encryption, auditing).