Role Description You will join one of our high-performing consultant teams, delivering cutting-edge solutions for our financial services clients. This is a hands-on role where your data analysis skills and financial acumen will be essential to supporting senior business experts. You will be moving beyond the limitations of standard tools like Excel to solve complex data challenges. Working directly with critical data in the reinsurance domain, performing data crunching & providing actionable insights that inform key decisions. Key Responsibilities - Perform complex data manipulation and analysis to support senior business experts and stakeholders. - Utilise Python and its data libraries to handle large datasets and automate analytical tasks. - Design and execute robust data mapping and analysis projects. - Work with our expert teams to understand client needs and translate them into effective data solutions. - Working in a fast paced start-up agile and high pressure environment. - DBT: Transforming raw data in the warehouse into clean, tested & documented models using SQL. - Databricks: Must have recent experience. - Domain knowledge: Have worked within the operations team for an asset management department and understands assets data, like financial securities, trading data within the last 3 years. - Investment & Financial: Experience with investment-related projects or working in the reinsurance or life insurance domain. - SQL: Data manipulation and querying. - Python: Experience in libraries like Pandas and NumPy. - Communication: Excellent senior stakeholder, articulate, presenting & engagement skills. Requirements - Fully Remote - Outside IR35 - Long term contract - c£500/Day

Company Description About Syngenta At Syngenta Crop Protection, we're pioneering solutions that safeguard global food security while championing sustainable agriculture. As a world market leader headquartered in Switzerland, we empower farmers with innovative crop protection technologies that defend against nature's toughest challenges. We unite advanced science with digital solutions to develop intelligent crop protection that maximizes yields while minimizing environmental impact. Join our mission of revolutionizing plant protection from seed to harvest. Job Description Support analytical data infrastructure providing application, tool, and ad-hoc access to large datasets consisting of complex data types including genomic, phenotypic, environmental, image and geospatial data. Engage with business stakeholders to understand strategic roadmaps and build a technical strategy to support them. Interface with DevOps teams to extract, load, and transform data from a wide variety of data sources using big data technologies. Deploy high value, high performance datasets. Create and support real-time data pipelines built on AWS technologies. Embed quality and intelligent reporting capabilities into data pipelines including detection of anomalies and changes in trends with meaningful alerts and statistics. Provide new capabilities and increase efficiency by continually researching the latest big data and visualization technologies. Collaborate with data scientists and other tech teams to implement advanced analytics algorithms into data pipelines that exploit rich datasets for statistical analysis, prediction, clustering, and machine learning. Improve automation and simplifying data as a service. Role is fully remote. Qualifications This position requires a Bachelor’s degree in Engineering (Any), Computer Science, or a related field of study and five (5) of experience in the position offered or five (5) years of experience as a Data Engineer, Software Engineer, Technology Analyst, or a closely related occupation. Requires five (5) years of experience with: - Working with business and technical stakeholders to implement end‐to‐end data solutions including data models, data pipelines, and business intelligence products - Designing and implementing DW Architectures, OLAP technologies, Star schema, Snowflake schema, and Aggregation Techniques. - Gathering requirements and building custom BI and big data reporting solutions using Tableau, OBIEE, Looker, or PowerBI. - Architecting database management systems with Oracle, SQL Server, Postgres, Spark, or Hadoop. - Working with data structures and algorithms. - Programming and scripting languages including Shell, Python, Scala, or PySpark. Requires three (3) years of experience with: - Designing and building real time analytics platform with latencies less than a minute using Kafka, Spark, AWS, or GCP. - Working with Datadog to identity issues with data quality and anomalies, and setup alerting. - Setting up custom frameworks to identify data quality issues and introduce self-service Business Intelligence capabilities using CI/CD tools including Buildkite, Jenkins, or Gitlab. - Collaborating with Data Scientists and building data products that help machine learning teams in clustering, prediction, and statistical analysis. - Building data pipelines to extract load and transform data using big data technologies including BigQuery, Redshift, BigTable, Aerospike, Dataproc, or EMR. - Must pass a background check and drug test before beginning employment. Additional Information What We Offer: - A culture that celebrates belonging and collaboration, promotes professional development, and strives for a work-life balance that supports the team members. offers flexible work options to support your work and personal needs - Full Benefit Package (Medical, Dental & Vision) that starts on your first day. - 401k plan with company match, Profit Sharing & Retirement Savings Contribution. - Paid Vacation, 9 Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits. Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. Family and Medical Leave Act (FMLA) (http://www.dol.gov/whd/regs/compliance/posters/fmla.htm) Equal Employment Opportunity Commission's (EEOC) (http://webapps.dol.gov/elaws/firststep/poster_direct.htm) Employee Polygraph Protection Act (EPPA) (http://www.dol.gov/whd/regs/compliance/posters/eppa.htm) #LI-DNI

Role Description This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Staff Data Engineer - REMOTE. You will play a crucial role in shaping the backbone of the data platform, ensuring security, reliability, and trust while managing vast volumes of data from workouts and health-related sources. Your expertise will directly impact product innovation, AI, and analytics, allowing our partner to continue redefining the training experience. By building and scaling data systems, you'll contribute to personalizing experiences and enhancing coaching. Your work will ensure that sensitive information is protected while maintaining compliance with an ever-evolving regulatory landscape. - Architect secure and scalable data systems that support growth and meet regulatory standards. - Build and optimize data models and pipelines across diverse sources: sensors, workouts, and health integrations. - Establish controls for access, encryption, anonymization, monitoring, and auditability. - Define and enforce best practices for managing sensitive data, including PHI and PII. - Collaborate with teams across various departments to translate needs into compliant solutions. - Conduct risk assessments and implement safeguards guided by NIST frameworks. - Support SOC 2 audits by documenting and demonstrating effective security controls. - Mentor engineers and scientists, setting high standards for secure data engineering. - Continuously evolve the platform, introducing tools and frameworks to balance innovation with regulatory compliance. Qualifications - 8+ years of experience in data engineering, or 6+ years with a Master’s degree (or equivalent). - Strong skills in SQL, Python, and distributed data processing (Spark, Databricks, or similar). - Experience building pipelines with DBT, Airflow, Fivetran, or related tools. - Background in data modeling and warehousing with systems like Snowflake, Databricks, or Redshift. - Hands-on experience working with regulated environments and sensitive data. - Familiarity with frameworks such as HIPAA, SOC 2, and NIST for security and compliance. - Skilled in access control design, audit logging, encryption, and governance. - Excellent communicator who can explain complex tradeoffs to both technical and non-technical audiences. - Known for technical leadership and mentoring, raising the bar for engineering quality. Benefits - Flexible work schedule with remote opportunities. - Collaborative work environment with cross-functional teams. - Opportunities for professional growth and development. - Inclusive culture that celebrates diversity and nurtures unique perspectives. - Support for accessible work arrangements and accommodations. - Innovative projects that leverage cutting-edge technology and data. - Participation in ongoing training and skill enhancement programs.

• Own the technical design of Nebula — Vibe's identity graph, entity resolution pipelines, and data clean room integrations with partners including major broadcasters. • Define the end-to-end data architecture serving Analytics, ML, and real-time bidding systems. • Solve training vs. inference skew: ensure data used to train models matches data available at bid time. • Design and ship the ML platform — feature stores, model registries, and CI/CD for ML — so data scientists can deploy models to production without infrastructure blockers. • Own the "golden path": a data scientist pushes code, a model retrains and deploys automatically. • Bridge the gap between Data Engineering and Data Science; remove friction, not just document it. • Champion the transition to a security-first culture — SOC2, RBAC, PII anonymization — without turning compliance into a bottleneck. • Build guardrails, not gatekeepers: automated policy checks that let engineers ship fast and safely. • Own data retention policies, access controls, and governance frameworks across 200+ data assets. • Hold the Data Platform P&L — track unit economics, separate storage costs from ML training costs, and ensure spend scales with revenue rather than ahead of it. • Optimise across a hybrid stack: high-volume streaming (Kafka/Kinesis), log storage (S3/Athena), and GPU compute for ML training. • Identify waste fast; distinguish inefficiency from intentional growth investment. • Manage and grow the Data Platform Engineering team. • Assess current team capabilities against what's needed to ship Nebula and the ML platform. • Build a culture where engineers adopt structure because it makes them faster, not because they're told to.