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🔥 Software Engineer 2

Posted about 1 hour ago

📍 UK

🔍 Cybersecurity

🏢 Company: Abnormal👥 501-1000💰 $250,000,000 Series D 10 months agoArtificial Intelligence (AI)Email Information Technology Cyber Security Network Security

🔧 Requirements

Streaming data systems - using Kafka, Spark, Map/Reduce or similar to process large data sets
Experience with building and operating distributed systems and services at a high scale (~billions of transactions each day)
Working with external party APIs
3-5 years of overall software engineering experience
Strong sense of best practices in developing software

💡 Responsibilities

Build out streaming infrastructure for our data integration platform
Be able to capture data from slack, teams and other streaming data platforms for processing within our Data Ingestion Platform (DIP)
Work to integrate customers into the new streaming infrastructure, migrating from the older polling model where necessary
Work with Product Managers, Designers & Account TakeOver (ATO) detection team on product requirements and frontend implementation
Partner with our ATO Detection team
Understand the workflows and processes of the ATO Detection team. Be an effective liaison between ATO Infrastructure <> ATO Detection to understand and represent ATO Detection team needs, and convert those needs into ATO Infrastructure team deliverables
Help build our group through excellent interview practices
Be a talent magnet - someone who through the interview process demonstrates their own strengths in a way that attracts candidates to Abnormal and to the ATO team and ensures that we close the candidates we want to close

Backend Development Python Software Development Cybersecurity Apache Kafka API testing Spark Communication Skills CI/CD RESTful APIs DevOps Microservices Software Engineering

Posted about 1 hour ago

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🔥 Engineering Manager (Data)

Posted about 17 hours ago

📍 Romania

🧭 Full-Time

🔍 Software Development

🏢 Company: Plain Concepts👥 251-500 Consulting Apps Mobile Apps Information Technology Mobile

🔧 Requirements

At least 3 years of experience as a Delivery Manager, Engineering Manager, or similar role in software, data-intensive or analytics projects.
Proven experience managing client relationships and navigating stakeholder expectations.
Strong technical background in Data Engineering (e.g., Python, Spark, SQL) and Cloud Data Platforms (e.g., Azure Data Services, AWS, or similar).
Solid understanding of scalable software and data architectures, CI/CD practices for data pipelines, and cloud-native data solutions.
Experience with data pipelines, sensor integration, edge computing, or real-time analytics is a big plus.
Ability to read, write, and discuss technical documentation with confidence.
Strong analytical and consultative skills to identify impactful opportunities.
Agile mindset, always focused on delivering real value fast.
Conflict resolution skills and a proactive approach to identifying and mitigating risks.

💡 Responsibilities

Understanding the business and technical objectives of data-driven projects.
Leading multidisciplinary teams to deliver scalable and robust software and data solutions on time and within budget.
Maintaining proactive and transparent communication with clients, helping them understand the impact of data products.
Supporting the team during key client interactions and solution presentations.
Designing scalable architectures for data ingestion, processing, and analytics.
Collaborating with data engineers, analysts, and data scientists to align solutions with client needs.
Ensuring the quality and scalability of data solutions and deliverables across cloud environments.
Analyzing system performance and recommending improvements using data-driven insights.
Providing hands-on technical guidance and mentorship to your team and clients when needed

AWS Python SQL Agile Cloud Computing Azure Data engineering Spark Communication Skills CI/CD Client relationship management Team management Stakeholder management Data analytics

Posted about 17 hours ago

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🔥 Data Platform Engineer

Posted about 21 hours ago

📍 United States

🧭 Full-Time

💸 180000.0 - 220000.0 USD per year

🔍 Software Development

🏢 Company: Prepared👥 51-100💰 $27,000,000 Series B 8 months agoEnterprise Software Public Safety

🔧 Requirements

5+ years of experience in data engineering, software engineering with a data focus, data science, or a related role
Knowledge of designing data pipelines from a variety of source (e.g. streaming, flat files, APIs)
Proficiency in SQL and experience with relational databases (e.g., PostgreSQL)
Experience with real-time data processing frameworks (e.g., Apache Kafka, Spark Streaming, Flink, Pulsar, Redpanda)
Strong programming skills in common data-focused languages (e.g., Python, Scala)
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Prefect, Temporal)
Familiarity with AWS-based data solutions
Strong understanding of data warehousing concepts and technologies (Snowflake)
Experience documenting data dependency maps and data lineage
Strong communication and collaboration skills
Ability to work independently and take initiative
Proficiency in containerization and orchestration tools (e.g., Docker, Kubernetes)

💡 Responsibilities

Design, implement, and maintain scalable data pipelines and infrastructure
Collaborate with software engineers, product managers, customer success managers, and others across the business to understand data requirements
Optimize and manage our data storage solutions
Ensure data quality, reliability, and security across the data lifecycle
Develop and maintain ETL processes and frameworks
Work with stakeholders to define data availability SLAs
Create and manage data models to support business intelligence and analytics

AWS Docker PostgreSQL Python SQL Apache Airflow ETL Kubernetes Snowflake Apache Kafka Data engineering Spark Scala Data modeling

Posted about 21 hours ago

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🔥 Senior Backend Engineer

Posted 1 day ago

📍 United States

🧭 Full-Time

🔍 Software Development

🔧 Requirements

7+ years of experience related to Java development (Kotlin preferred) in addition to data engineering and modeling complex data
Strong experience in SQL, data modeling, and manipulating and extracting large data sets.
Hands-on experience working with data warehouse technologies.
Experience building high-quality APIs and working with microservices (Spring Boot, REST).
Experience with cloud infrastructure and containerization (Docker, Kubernetes).
Proficiency with Git, CI/CD pipelines, and build tools (Gradle preferred).

💡 Responsibilities

Work with your engineering squad to design and build a robust platform that will handle terabytes of real-time and batch data flowing through internal and external systems.
Build high volume and low latency services that are reliable at scale.
Create and manage ETL/ELT workflows that transform our billions of raw data points daily into quickly accessible information across our databases and data warehouses
Develop big data solutions using commercial and open-source frameworks.
Collaborate with and explain complex technical issues to your technical peers and non-technical stakeholders.

Backend Development Docker SQL Cloud Computing Design Patterns ETL Git Java Kafka Kotlin Kubernetes Spring Boot Algorithms API testing Data engineering Data Structures REST API Spark CI/CD RESTful APIs Microservices Data modeling

Posted 1 day ago

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🔥 Senior Data Scientist, US

Posted 1 day ago

📍 Texas, Denver, CO

💸 148000.0 - 189000.0 USD per year

🔍 SaaS

🏢 Company: Branch Metrics

🔧 Requirements

4+ years of relevant experience in data science, analytics, or related fields.
Degree in Statistics, Mathematics, Computer Science, or related field.
Proficiency with Python, SQL, Spark, Bazel, CLI (Bash/Zsh).
Expertise in Spark, Presto, Airflow, Docker, Kafka, Jupyter.
Strong knowledge of ML frameworks (scikit-learn, pandas, xgboost, lightgbm).
Experience deploying models to production on AWS infrastructure and experience with the basic AWS services.
Advanced statistical knowledge (regression, A/B testing, Multi-Armed Bandits, time-series anomaly detection).

💡 Responsibilities

Collaborate with stakeholders to identify data-driven business opportunities.
Perform data mining, analytics, and predictive modeling to optimize business outcomes.
Conduct extensive research and evaluate innovative approaches for new product initiatives.
Develop, deploy, and monitor custom models and algorithms.
Deliver end-to-end production-ready solutions through close collaboration with engineering and product teams.
Identify opportunities to measure and monitor key performance metrics, assessing the effectiveness of existing ML-based products.
Serve as a cross-functional advisor, proposing innovative solutions and guiding product and engineering teams toward the best approaches.
Anticipate and clearly articulate potential risks in ML-driven products.
Effectively integrate solutions into existing engineering infrastructure.

AWS Docker Python SQL Bash Kafka Machine Learning Airflow Regression testing Pandas Spark RESTful APIs Time Management A/B testing

Posted 1 day ago

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🔥 Sr Data Scientist

Posted 2 days ago

📍 United States

🧭 Full-Time

💸 211536.0 - 287100.0 USD per year

🔍 Software Development

🏢 Company: jobs

🔧 Requirements

SQL and Python programming to query and validate the accuracy of datasets
Design and develop workflow orchestration tools
Python scripting to develop statistical and machine learning models for classification
Use agile software development principle to design, plan and structure deployment of software products
Develop machine learning models to segment customer behavior, identify market concentration and volatility using Python and Spark ML
Building KPIs (Key Performance Indicators) and metrics, validating using statistical hypothesis testing
Expertise in Cloud Computing resources and maintaining data on cloud storage
Big Data processing for data cleaning
Deploy self-serving data visualization tools, automating, generating reports and consolidating visually on tableau dashboards
Develop data engineering pipelines and transformations

💡 Responsibilities

Lead, build and implement analytics functions for Honey features
Conduct impactful data analysis to improve customer experiences and inform product development
Collaborate cross-functional support teams to build world-class products and design hypothesis-driven experiments
Gather and collate business performance and metrics to recommend improvements, automation, and data science directives for overall business performance
Present findings and recommendations to senior level/non-technical stakeholders
Maintain large datasets by performing batch scheduling and pipelining ETL operations
Perform ad-hoc exploratory analysis on datasets to generate insights and automate production ready solutions
Develop machine learning-based models to improve forecasting and predictive analytics
Implement innovative quantitative analyses, test new data wrangling techniques, and experiment with new visualization tools to deliver scalable analytics
Develop and create programming paradigms and utilizing tools like git, data structures, OOP, and network algorithms

Python SQL Cloud Computing Data Analysis ETL Git Machine Learning Numpy Tableau Algorithms Data engineering Data Structures Pandas Spark Tensorflow Agile methodologies Data visualization Data modeling

Posted 2 days ago

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🔥 Staff Data Engineer

Posted 2 days ago

📍 United States

🧭 Full-Time

💸 160000.0 - 230000.0 USD per year

🔍 Daily Fantasy Sports

🔧 Requirements

7+ years of experience in a data Engineering, or data-oriented software engineering role creating and pushing end-to-end data engineering pipelines.
3+ years of experience acting as technical lead and providing mentorship and feedback to junior engineers.
Extensive experience building and optimizing cloud-based data streaming pipelines and infrastructure.
Extensive experience exposing real-time predictive model outputs to production-grade systems leveraging large-scale distributed data processing and model training.
Experience in most of the following: SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable, Materialize, AlloyDB, etc
Replication/ELT services: Data Stream, Hevo, etc.
Data Transformation services: Spark, Dataproc, etc
Scripting languages: SQL, Python, Go.
Cloud platform services in GCP and analogous systems: Cloud Storage, Cloud Compute Engine, Cloud Functions, Kubernetes Engine etc.
Data Processing and Messaging Systems: Kafka, Pulsar, Flink
Code version control: Git
Data pipeline and workflow tools: Argo, Airflow, Cloud Composer.
Monitoring and Observability platforms: Prometheus, Grafana, ELK stack, Datadog
Infrastructure as Code platforms: Terraform, Google Cloud Deployment Manager.
Other platform tools such as Redis, FastAPI, and Streamlit.
Excellent organizational, communication, presentation, and collaboration experience with organizational technical and non-technical teams
Graduate degree in Computer Science, Mathematics, Informatics, Information Systems or other quantitative field

💡 Responsibilities

Enhance the capabilities of our existing Core Data Platform and develop new integrations with both internal and external APIs within the Data organization.
Develop and maintain advanced data pipelines and transformation logic using Python and Go, ensuring efficient and reliable data processing.
Collaborate with Data Scientists and Data Science Engineers to support the needs of advanced ML development.
Collaborate with Analytics Engineers to enhance data transformation processes, streamline CI/CD pipelines, and optimize team collaboration workflows Using DBT.
Work closely with DevOps and Infrastructure teams to ensure the maturity and success of the Core Data platform.
Guide teams in implementing and maintaining comprehensive monitoring, alerting, and documentation practices, and coordinate with Engineering teams to ensure continuous feature availability.
Design and implement Infrastructure as Code (IaC) solutions to automate and streamline data infrastructure deployment, ensuring scalable, consistent configurations aligned with data engineering best practices.
Build and maintain CI/CD pipelines to automate the deployment of data solutions, ensuring robust testing, seamless integration, and adherence to best practices in version control, automation, and quality assurance.
Experienced in designing and automating data governance workflows and tool integrations across complex environments, ensuring data integrity and protection throughout the data lifecycle.
Serve as a Staff Engineer within the broader PrizePicks technology organization by staying current with emerging technologies, implementing innovative solutions, and sharing knowledge and best practices with junior team members and collaborators.
Ensure code is thoroughly tested, effectively integrated, and efficiently deployed, in alignment with industry best practices for version control, automation, and quality assurance.
Mentor and support junior engineers by providing guidance, coaching and educational opportunities
Provide on-call support as part of a shared rotation between the Data and Analytics Engineering teams to maintain system reliability and respond to critical issues.

Leadership Python SQL Cloud Computing ETL GCP Git Kafka Kubernetes Airflow Data engineering Go Postgres REST API Spark CI/CD Mentoring DevOps Terraform Data visualization Data modeling Scripting

Posted 2 days ago

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🔥 Staff Data Engineer

Posted 3 days ago

📍 United States, Canada

🧭 Full-Time

💸 158000.0 - 239000.0 USD per year

🔍 Software Development

🏢 Company: 1Password

🔧 Requirements

Minimum of 8+ years of professional software engineering experience.
Minimum of 7 years technical engineering experience building data processing applications (batch and streaming) with coding in languages.
In-depth, hands-on experience on extensible data modeling, query optimizations and work in Java, Scala, Python, and related technologies.
Experience in data modeling across external facing product insights and business processes, such as revenue/sales operations, finance, and marketing.
Experience with Big Data query engines such as Hive, Presto, Trino, Spark.
Experience with data stores such as Redshift, MySQL, Postgres, Snowflake, etc.
Experience using Realtime technologies like Apache Kafka, Kinesis, Flink, etc.
Experience building scalable services on top of public cloud infrastructure like Azure, AWS, or GCP with extensive use of datastores like RDBMS, key-value stores, etc.
Experience leveraging distributed systems at scale and systems knowledge on infrastructure hardware, resources bare-metal hosts to containers to networking.

💡 Responsibilities

Design, develop, and automate large-scale, high-performance batch and streaming data processing systems to drive business growth and enhance product experience.
Build data engineering strategy that supports a rapidly growing tech company and aligns with the priorities across our product strategy and internal business organizations’ desire to leverage data for more competitive advantages.
Build scalable data pipelines using best-in-class software engineering practices.
Develop optimal data models for storage and retrieval, meeting critical product and business requirements.
Establish and execute short and long-term architectural roadmaps in collaboration with Analytics, Data Platform, Business Systems, Engineering, Privacy and Security.
Lead efforts on continuous improvement to the efficiency and flexibility of the data, platform, and services.
Mentor Analytics & Data Engineers on best practices, standards and forward-looking approaches on building robust, extensible and reusable data solutions.
Influence and evangelize high standard of code quality, system reliability, and performance.

AWS Python SQL ETL GCP Java Kubernetes MySQL Snowflake Algorithms Apache Kafka Azure Data engineering Data Structures Postgres RDBMS Spark CI/CD RESTful APIs Mentoring Scala Data visualization Data modeling Software Engineering Data analytics Data management

Posted 3 days ago

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🔥 QL: Sn Data Scientist (YB S)

Posted 3 days ago

📍 Ukraine

🧭 Full-Time

🔍 SaaS

🏢 Company: Adaptiq👥 51-100 Consulting Professional Services Software

🔧 Requirements

A Master’s or PhD in Computer Science, Physics, Applied Mathematics or a related field, demonstrating a strong foundation in analytical thinking.
At least 5 years of professional experience in end-to-end machine learning lifecycle (design, development, deployment, and monitoring).
At least 5 years of professional experience with Python development, including OOP, writing production-grade code, testing, and optimization.
At least 5 years of experience with data mining, statistical analysis, and effective data visualization techniques.
Deep familiarity with modern ML/DL methods and frameworks (e.g., PyTorch, XGBoost, scikit-learn, statsmodels).

💡 Responsibilities

Develop and Optimize Advanced ML Models: Build, improve, and deploy machine learning and statistical models for forecasting demand, analyzing price elasticities, and recommending optimal pricing strategies.
Lead End-to-End Data Science Projects: Own your projects fully, from conceptualization and experimentation through production deployment, monitoring, and iterative improvement.
Innovate with Generative and Predictive AI Solutions: Leverage state-of-the-art generative and predictive modeling techniques to automate complex pricing scenarios and adapt to rapidly changing market dynamics.

AWS Python SQL Apache Hadoop Data Analysis Data Mining Machine Learning PyTorch Spark CI/CD Data visualization

Posted 3 days ago

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🔥 Lead Software Engineer - AI Data Systems

Posted 3 days ago

📍 21 U.S. states

🧭 Full-Time

💸 151500.0 - 215500.0 USD per year

🔍 Software Development

🏢 Company: Upwork👥 501-1000💰 over 8 years ago🫂 Last layoff about 2 years agoMarketplace Freelance Copywriting Peer to Peer

🔧 Requirements

Strong software engineering background with deep experience in building data collection, transformation, and featurization pipelines at scale.
Proficiency in Python, including async programming and concurrency tools, as well as data-centric frameworks such as Pandas, Spark, or Apache Beam.
Familiarity with ML model development workflows and infrastructure, including dataset versioning, experiment tracking, and model evaluation.
Experience deploying and scaling AI systems in cloud environments such as AWS, GCP, or Azure.
Proven success operating in highly ambiguous environments such as research labs, startups, or fast-paced product teams.
A track record of working with or alongside high-caliber peers in top engineering teams, research groups, or startup ecosystems.
Growth mindset, strong communication skills, and a commitment to inclusive collaboration and continuous learning.

💡 Responsibilities

Design and implement systems to collect and curate high-quality training datasets for supervised, unsupervised, and reinforcement learning use cases.
Build scalable featurization and preprocessing pipelines to transform raw data into structured inputs for AI/ML model development.
Partner with ML engineers and researchers to define data requirements and production workflows that support LLM-based agents and autonomous AI systems.
Lead the development of infrastructure that enables experimentation, evaluation, and deployment of machine learning models in production environments.
Support orchestration and real-time inference pipelines using Python and modern cloud-native tools, ensuring low-latency and high availability.
Mentor engineers and foster a high-performance, collaborative engineering culture grounded in technical excellence and curiosity.
Drive cross-functional alignment with product, infrastructure, and research stakeholders, ensuring clarity on progress, goals, and architecture.

AWS Docker Leadership Python SQL Apache Airflow Cloud Computing Git Kubernetes Machine Learning Algorithms Data engineering Data science Data Structures REST API Pandas Spark Communication Skills CI/CD Mentoring Teamwork Software Engineering

Posted 3 days ago

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