Role Description We’re looking for an experienced Senior Data Engineer/Analyst to lead the design and delivery of production-grade data platforms, pipelines, and analytics solutions that drive business intelligence and AI/ML capabilities across the organization. In this role, you will own critical data workstreams end to end, make key architectural decisions on data modeling, pipeline design, and platform selection, and collaborate with clients and stakeholders to translate business requirements into scalable data solutions. You will champion modern data stack practices using Snowflake, Databricks, dbt, and cloud-native services, while mentoring other engineers and driving the maturity of our data engineering, analytics, and DataOps capabilities. What You’ll Do - Lead the design, development, and optimization of enterprise-grade data pipelines and transformation layers using dbt, Apache Spark, Airflow, Dagster, and cloud-native orchestration services. - Architect and manage data platforms on Snowflake and/or Databricks, including warehouse design, lakehouse architecture, compute optimization, access governance, and cost management. - Define and enforce data modeling standards across the organization using Kimball dimensional modeling, Data Vault 2.0, Activity Schema, or hybrid approaches. - Design and implement end-to-end data ingestion strategies from diverse sources: transactional databases (CDC via Debezium, Fivetran), APIs, event streams (Kafka, Kinesis, Spark Structured Streaming), SaaS platforms, and unstructured data sources. - Build and maintain curated data products, metrics layers, and semantic models that enable self-service analytics across the organization. - Implement comprehensive data quality, observability, and lineage frameworks using dbt tests, Great Expectations, Monte Carlo, Datafold, Elementary, or Soda. - Collaborate with clients and business stakeholders to gather requirements, translate them into data architecture decisions, and drive technical strategy from ideation to production. - Lead the adoption of DataOps practices including CI/CD for data pipelines (GitHub Actions, dbt Cloud, Databricks Asset Bundles), automated testing, and environment promotion workflows. - Design and optimize analytics solutions and advanced dashboards using Tableau, Looker, Power BI, or Sigma Computing, ensuring performance and usability at scale. - Support AI/ML initiatives by designing and maintaining feature stores, training datasets, and model input/output data pipelines in collaboration with ML engineers. - Mentor junior and mid-level data engineers and analysts, conduct architecture and code reviews, and drive knowledge sharing across the team. - Drive data governance initiatives including cataloging (Alation, Atlan, DataHub, Unity Catalog), lineage tracking, PII management, and regulatory compliance (GDPR, CCPA, HIPAA). Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. - 5–8+ years of professional experience in data engineering, analytics engineering, or a closely related role with significant production delivery. - Deep expertise in SQL and advanced data transformation techniques across analytical databases and data warehouses. - Extensive hands-on experience with Snowflake and/or Databricks in production environments, including architecture design, performance tuning, and cost optimization. - Strong experience with dbt (data build tool) for production-grade transformation, testing, documentation, and CI/CD workflows. - Proficiency in Python (Pandas, PySpark, Polars) and Apache Spark for large-scale data processing. - Experience designing and operating workflow orchestration using Airflow, Dagster, Prefect, or cloud-native equivalents. - Strong knowledge of data ingestion patterns and tools: Fivetran, Airbyte, CDC (Debezium), streaming (Kafka, Kinesis, Flink). - Experience with data visualization and BI platforms: Tableau, Looker, Power BI, or Sigma at enterprise scale. - Proven ability to work directly with clients and stakeholders and translate ambiguous business requirements into concrete data solutions. - Demonstrated ability to mentor engineers, lead design reviews, and influence data architecture direction. - Strong understanding of data governance, cataloging, data quality, and compliance frameworks. Preferred Qualifications - Snowflake SnowPro Advanced or Architect, Databricks Data Engineer Professional, or AWS Data Analytics Specialty certification. - Experience with lakehouse architectures using Delta Lake, Apache Iceberg, or Apache Hudi. - Familiarity with real-time analytics and streaming architectures: Kafka, Spark Structured Streaming, Flink, Materialize, or ksqlDB. - Experience with metrics/semantic layers at scale: dbt Semantic Layer, Cube, MetricFlow, or LookML. - Experience designing data platforms that serve AI/ML workloads, including feature stores (Feast, Tecton), vector databases (Pinecone, Weaviate), and RAG data pipelines. - Familiarity with data mesh or data product architecture patterns in large organizations. - Experience with cloud data infrastructure across AWS (S3, Glue, Athena, Lake Formation, Redshift Serverless), Azure (ADLS, Synapse, Fabric, Purview), or GCP (BigQuery, Dataflow, Dataplex). - Knowledge of cost optimization and FinOps for data platforms (Snowflake credit management, Databricks cluster policies, spot instances). Benefits - Unlimited PTO. - Very generous parental leave, much above industry standards. - Entrepreneurial culture where pushing limits and taking risks is everyday business. - Open communication with management and company leadership. - Small, dynamic teams = massive impact. - Medical, Dental and Vision coverage for employees. - Access to Disability & Life insurance. - Mental health and wellbeing support. - Annual bonus program. - Employer Stock Purchase Program (ESPP). - Yearly Team building experiences. - Mentorship and sponsorship opportunities. - Manager resources and support. Salary Range US East/West Coast: $121,700 - $162,200 Disclaimer: These salary ranges are estimates based on market data. Actual compensation may vary depending on factors including work experience, education, skills, and specific geographic location. Company Description We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic.

About Us At Verisian, we build deep tech and AI solutions that enable groundbreaking medical therapies to enter the market faster and safely. New medicines and devices are judged using all available evidence in the proper context and backed by automated analyses that validate and make data and results transparent to companies and regulators alike. Regulators will make the right decisions on novel therapies faster and with greater confidence, protecting patients from harm and making breakthrough treatments available as soon as is possible and safe. In times of increasing trial numbers and complexity, we are building technical innovation to remove crucial bottlenecks for pharmaceutical and medical device companies, as well as public health authorities, directly affecting clinical trial planning, analyses, validation, submission, and market approval. By joining us, you will commit yourself to building software and AI tools that directly contribute to increasing the rate at which medical innovation improves human health and wellbeing. Disclaimer: We welcome candidates of diverse experience levels, and meeting every requirement is not mandatory. Research indicates that underrepresented groups tend to apply only if they meet all qualifications. If you're enthusiastic about the role, please apply and let our recruiters evaluate your application. Culture At Verisian, our mission is to build the future infrastructure of medical innovation. To help us succeed, we're creating a unique employee culture. We always put the mission first. We're fanatically customer obsessed, crafting world-class products that customers love with every interaction. We take extreme ownership and accountability of our work, seeing whatever we do through to completion. We communicate candidly and directly with each other, even when it's uncomfortable. We're innately curious, open to alternative perspectives and invest passionately in our own continuous growth. Role Description As a Data Engineer, you will join our world-class engineering team in building the Verisian Platform. You will work on an application that exposes our clinical trial insights to data managers, statistical programmers, statisticians, medical experts/writers, and regulatory authorities.  The Verisian Platform brings value to a set of highly regulated processes crucial for medical progress and innovation. Your work will support the Planner, Builder, Explorer, Validator, Submitter, and related supporting modules. These core modules of the platform target the planning, exploration/onboarding, building, validation, submission, and review of clinical trials and their results. They enable data managers, statistical programmers, statisticians, medical writers, and regulators to deliver their work faster, at higher quality, lower cost, and in greater confidence. Our pipelines analyze clinical trial documentation, code, logs, data, and results to build a knowledge graph through code traceability. We harness the resulting dataset, column-level and logic lineage to turn clinical trials into Information Infrastructure that can be used by experts and consumed by AI to revolutionize how therapies are evaluated and enter the market. We capture complex processes in fully- and semi-automated workflows that place experts in control and AI automation at their fingertips. We build visualizations to provide our customers with a maximum of insight as fast as possible. Our application stack is based on Next.js and deployed via Docker/Kubernetes in the cloud. The data analysis pipelines run in Argo Workflows. We analyze code based on Antlr4 and Java. AI agents are developed in Python. The data analysis engine is developed in Python. Git is where our code lives, and Github Actions is how it gets out into the world. In tandem, you will create data validation rules (custom DSL) and develop our data analysis engine (Python) that is used to automatically detect inconsistencies and analysis errors in data while enforcing regulatory data standard adherence. These crucial pipelines are an integral part of our platform to expose our game-changing functionality to users and consumed by our AI agents to automate the planning, analysis, validation, and submission of clinical trials. You will be expected to lead the analysis, design, building and testing of components of the engine and data validation rules. As part of our core team, you will join us in designing, prioritising, building and testing new functionality, troubleshooting customer issues, finding root causes, and deploying required fixes to ensure maximal user impact and performance.

Role Description Are you passionate about leading a team of skilled engineers in a fast-paced, innovative FinTech environment? Green Street is seeking an experienced Lead Data Engineer to manage and mentor a team dedicated to designing and building robust data pipelines and architecture for the Commercial Real Estate (CRE) industry. You’ll oversee the development of advanced data solutions, integrating both public and proprietary data from diverse sources to create comprehensive data products and insights for our clients and internal research teams. Why join Green Street? We’re a highly collaborative, Agile-driven team committed to engineering excellence, leveraging the latest technologies, and continuously optimizing our data infrastructure. As a leader on our team, you’ll have the opportunity to guide data engineers and QA engineers, shape Green Street's data solutions, and work closely with cross-functional teams to deliver meaningful impact within the CRE space. We love great engineers, and we are excited to get to know you better. Please share your resume today! Responsibilities - Lead and manage a team of data engineers and data QA engineers, fostering a collaborative and high-performance culture - Design and develop robust data ingestion, ETL processes, and ensure data integrity and high availability - Document and manage data models, schema designs, and ER diagrams, ensuring a well-architected and scalable data structure - Oversee the development and maintenance of data architecture, optimizing for performance and reliability - Collaborate with research and product teams to enhance our analytical capabilities and streamline workflows - Conduct analysis on large, aggregated datasets (e.g., demographic, geographic, GIS/spatial) - Coordinate effectively with both onshore and offshore development teams, ensuring alignment and meeting project objectives Qualifications - 10+ years of experience in data engineering, with at least 3 years in a leadership role managing engineering teams - Advanced proficiency in Python (8+ years) and experience with relational databases (MySQL, PostgreSQL, etc.) - Prior experience with Git and Agile methodologies - Strong expertise in data architecture, ETL processes, and data pipeline management - Proven ability to design and optimize database schemas for complex data domains - Experience with cloud platforms, particularly AWS (Amazon RDS, Containers) - Willingness and desire to keep up with new cloud technologies, languages, standards, and practices - Strong problem-solving skills, excellent communication abilities, attention to detail, and a commitment to continuous learning and improvement - Ability to work independently in a fast-paced, agile environment - Ability to collaborate effectively with a remote team - Ability to overlap 3-4 hours with the U.S. West Coast to participate in team discussions and lead cross-time-zone collaboration Nice-to-Haves - Prior knowledge of finance, Real Estate, mathematics, GIS / spatial data is a plus - Experience with data quality best practices and automated data validation processes

• Lead the design and implementation of scalable, secure and resilient data systems across streaming, batch and real-time use cases. • Architect data pipelines, model and storage solutions that power analytical and product use cases; using primarily Python and SQL via orchestration tooling that run workloads in the cloud. • Leverage AI to automate both data processing and engineering processes. • Assure and drive best practices relating to data infrastructure, governance, security and observability. • Work with technologists across multiple teams to deliver coherent features and data outcomes. • Support the data team to help adopt data engineering principles. • Identify, validate and promote new tools and technologies that improve the performance and stability of data services.