Data Engineer

• Lead the end-to-end design, development, deployment, and support of scalable, production-grade data integration and engineering solutions
• Create and maintain technical designs and documentation aligned with business requirements, data architecture, and enterprise standards
• Collaborate with technology leadership to evaluate and implement new tools, technologies, and best practices that improve platform performance and user experience
• Partner with Product Management and business stakeholders to deliver data solutions that meet business objectives and design requirements
• Build strong cross-functional relationships, effectively communicating technical solutions and driving stakeholder alignment
• Mentor junior engineers, guide architectural decisions, and balance technical tradeoffs to deliver reliable and intuitive data products
• Drive operational excellence through monitoring, optimization, troubleshooting, and ongoing support of data platforms and services

Lead Data Engineer

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC, SFMC, 3rd Party SaaS, and SAP
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Build audience definitions and activation paths for on-site personalization, SFMC campaigns, CRM actions, and downstream systems
• Produce runbooks, onboarding guides, and self-service tooling for marketers, CRM owners, and platform teams.
• Lead cross-functional teams to deliver CDP capabilities.

Customer Data Platform Lead

• Own the strategy, design, and operational rollout of a Customer Data Platform (CDP) capability
• Drive capture, unification, activation, and governance of customer and digital touchpoint data
• Architect identity resolution and profile management approaches
• Lead integrations with SFDC (CRM), SFMC (Marketing Automation), 3rd Party SaaS, and SAP (ERP)
• Partner with analytics, privacy, and commercial stakeholders to enable real-time personalization
• Define the CDP vision, roadmap, and prioritized use cases aligned to commercial and digital objectives
• Establish canonical data models, event taxonomy, identity graphs, and profile schemas required for personalization and measurement
• Design robust data capture from Sitecore AI DXP, web/mobile events, product catalogs, clinical interactions, and other touchpoints
• Manage Sitecore CDP relationships and third-party integration partners

• Build strong partnerships with SPD experimentalists, process engineers, and analytical scientists to gather requirements for data solutions which will have direct pipeline impact
• Design and implement robust, scalable data pipelines that ingest experimental and process data from SPD teams
• Deliver analysis-ready datasets to support SPD digital initiatives, including process modeling and Bayesian optimization
• Define and enforce data standards, metadata schemas, and ontologies that make SPD data interoperable and readily consumable by downstream modeling and optimization workflows
• Automate data ingestion from laboratory instruments, electronic lab notebooks, PAT systems, and manufacturing systems and integrate with cloud-based storage and compute environments
• Apply and generate data analysis and visualization workflows
• Design and develop dashboards, reports, and data exports
• Curate data and support definition of needs for automation of data ingestion
• Influence digital data strategy for SPD by identifying opportunities to improve data capture practices at the source and reduce friction between experimentation and modeling
• Demonstrate excellent interpersonal, communication, and collaboration skills
• Embrace and model our core values including fostering a supportive culture where all can thrive
• Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment
• Perform impactful scientific innovation in a team-oriented manner that builds trusted partnerships across vast stakeholder networks
• Publish and present research, including maintaining an established track record of interaction with the broader academic community

Associate Principal Scientist, Data Engineer, Digital Insights

• Lead and manage ERP data migration projects across multiple client engagements.
• Oversee data extraction, transformation, loading, validation, and reconciliation activities.
• Develop migration plans, timelines, cutover strategies, and risk mitigation plans.
• Ensure migration deliverables are completed accurately, efficiently, and on schedule.
• Lead testing cycles, mock conversions, and go-live readiness reviews.
• Establish and maintain migration standards, methodologies, and quality controls.
• Develop validation and reconciliation frameworks to ensure data completeness and accuracy.
• Create repeatable migration templates, mapping standards, and automation tools.
• Define and monitor migration KPIs, quality metrics, and success criteria.
• Partner with clients to identify and resolve data quality issues prior to conversion.
• Serve as subject matter expert for ERP data migration strategy and execution.
• Design and oversee data mapping and transformation requirements.
• Support development of migration automation tools and ETL processes.
• Collaborate with implementation teams to align migration activities with project milestones.
• Maintain expertise across ERP platforms and evolving migration technologies.
• Lead, coach, and develop a team of migration specialists and data analysts.
• Establish delivery standards and promote continuous improvement.
• Support hiring, onboarding, workforce planning, and capability development.
• Create scalable processes that support growth of Accordant’s migration services practice.
• Serve as the primary migration leader for client engagements.
• Communicate migration status, risks, dependencies, and recommendations.
• Facilitate data workshops, mapping sessions, validation reviews, and cutover planning meetings.
• Build trusted relationships with client stakeholders and implementation teams.
• Support pre-sales discussions by providing migration assessments, estimates, and recommendations.

Data Migration Manager

• Work with a cross-functional team to optimize and implement our data strategy with a focus on optimization for digital transformation and the use of AI/ML
• Design and document end-to-end data architectures that support diverse analytic, operational, and research needs.
• Facilitate the implementation of a modern data platform (e.g. Snowflake, Databricks, etc.)
• Identify opportunities and implement solutions to increase data interoperability and standardization among systems and across other business units.
• Develop and implement pipelines to monitor and improve both internal and external (i.e., from CRO partners) data quality.
• Work with informatics and AI engineers optimizing the utility of data for their respective pipelines.
• Monitor and optimize the performance of data architectures and platforms.
• Develop or implement critical metrics to measure the impact of the overall data strategy.
• Stay up to date with the latest advances in the field and, as appropriate, evaluate them for adoption

Senior Manager, Artificial Intelligence Data Engineer

• Design and implement scalable, secure data platforms on Google Cloud using managed services (BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
• Build reusable frameworks and tooling (ingestion, transformation, quality, orchestration) that can be adopted by multiple product and domain teams.
• Enable self‑service data consumption and governance by standardizing patterns, templates, and platform capabilities rather than one‑off pipelines.
• Design logical and physical data platform architectures leveraging BigQuery, Dataflow/Apache Beam, Dataproc/Spark, Pub/Sub, and Cloud Storage.
• Define and implement standardized ingestion, transformation, and serving patterns (batch and streaming) as reusable blueprints.
• Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies).
• Build opinionated data ingestion frameworks (e.g., config‑driven pipelines, connectors, schema handling, error handling) on top of Dataflow, Dataproc, or Composer.
• Develop shared transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines).
• Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration.
• Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layers in BigQuery and related tools.
• Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards.
• Apply security and governance controls: IAM, VPC‑SC, CMEK, row/column‑level security, and policy‑driven access patterns
• Partner with domain data engineers, analytics, and ML teams to onboard use cases onto platform services and frameworks
• Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples.
• Contribute to platform roadmap, tool selection, and evaluation of new GCP services and open‑source components.

• Lead the design and implementation of scalable, secure data platforms on Google Cloud using managed services (BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer)
• Build reusable frameworks and tooling (ingestion, transformation, quality, orchestration) that can be adopted by multiple product and domain teams
• Develop shared transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines)
• Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration
• Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies)
• Build opinionated data ingestion frameworks (e.g., config‑driven pipelines, connectors, schema handling, error handling) on top of Dataflow, Dataproc, or Composer
• Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layers in BigQuery and related tools
• Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards
• Partner with domain data engineers, analytics, and ML teams to onboard use cases onto platform services and frameworks
• Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples
• Contribute to platform roadmap, tool selection, and evaluation of new GCP services and open‑source components

Lead Data Engineer – GCP Platform Services, Frameworks

• Support QVC and collaborate with an Agile team to build data pipelines and models
• Drive data-driven decision-making and personalization
• Shape both back-end data structures and how insights are delivered
• Design data architecture, models, APIs, and pipelines aligned with data architecture principles and team goals
• Solve complex data challenges to deliver insights that drive outcomes
• Create data products for analytics and data science teams to improve their productivity and insights
• Lead the evaluation, implementation, and deployment of new tools for analytics data engineering
• Develop and deliver plans on analytic data engineering capabilities and standards
• Partner with product teams and solution architects to develop scalable, reliable technical architectures
• Mentor peers through code reviews, collaboration, and technical coaching
• Explore and learn about machine learning, data science, AI, and personalization technologies to evolve our approach

• Design, build, and maintain saleable data pipelines within Snowflake from multiple sources
• Build and maintain medallion architecture (bronze/silver/gold layers) for data lake solutions
• Design data models that support both businesses intelligence and AI requirements
• Lead cloud-native data platform evolution, integrating Snowflake with AWS.
• Collaborate with business analysts, data scientists, and stakeholders to understand data requirements and translate them into technical specifications.
• Implement data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of data assets.
• Develop and maintain comprehensive documentation for data pipelines, models, and processes.
• Optimize query performance and resource utilization to reduce costs and improve processing times.
• Participate in on-call rotation to support production data systems and resolve incidents.

• Lead delivery of high-quality data solutions by partnering with stakeholders and coaching data engineers.
• Own end-to-end data engineering delivery across the project lifecycle.
• Build strong partnerships across the organization to align priorities and deliver data-related goals.
• Design clear, analytics-ready data structures by anticipating downstream analytical needs.
• Evaluate and adopt new technologies and data sources to improve capability and efficiency.
• Automate data ingestion and integration to reliably connect internal and external data sources.
• Document data sources, definitions, and technical solutions to support transparency and reuse.
• Reinforce strong delivery hygiene (version control, code review, automated testing, CI/CD, and operational readiness/monitoring).
• Apply agentic, AI-assisted coding practices to accelerate delivery while maintaining appropriate controls.
• Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access.
• Provide retrieval-ready context (RAG pipelines, vector stores, knowledge bases) when needed.

Manager, Data Engineer

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• Design scalable and secure Databricks Lakehouse architectures
• Lead implementation of data engineering, ingestion, transformation, and analytics frameworks
• Define enterprise data governance and security standards using Unity Catalog (UC)
• Architect CI/CD, DevOps, monitoring, and automation for Databricks environments
• Optimize Spark workloads, cluster utilization, and platform cost efficiency
• Collaborate with business, security, infrastructure, and data teams on enterprise data initiatives
• Design data integration patterns across cloud, APIs, MDM, and enterprise systems
• Support migration and modernization from legacy data platforms to Databricks
• Establish best practices for performance, scalability, governance, and operational excellence

Senior Data Architect

• Define the multi-year technical strategy for the process intelligence data platform, aligning platform capabilities with enterprise priorities in process mining, compliance, AI governance, and operational efficiency
• Lead the design and build of a scalable, multi-layered data architecture encompassing storage, semantic and relationship modeling, and AI-ready data layers on cloud infrastructure
• Design and deliver enterprise data lineage capabilities that trace data from source systems through transformation to consumption, and build automated data catalog generation to enable discoverability, impact analysis, and regulatory transparency
• Define and deliver governed data serving APIs that provide secure, performant, and well-documented access to curated datasets, knowledge graph queries, and AI-ready features for downstream consumers (analytics, AI/ML models, compliance tools)
• Define and deliver reusable platform services for state management, task orchestration, checkpointing, event logging, tool invocation traces, replay, evaluation datasets, and human-in-the-loop controls to support reliable production AI workflows
• Drive the technical integration of diverse operational data domains—including work execution data, process and procedure knowledge, workflow and operational systems, and AI agent observability data—into a unified, interconnected data model
• Recruit, develop, and lead a team of data engineers, knowledge graph engineers, ML engineers, and platform engineers; establish engineering standards, delivery practices, and a culture of technical excellence
• Collaborate with Process Intelligence leadership, Risk, Compliance, Operations, and AI Governance stakeholders to define data requirements, access models, and platform SLAs
• Establish data governance frameworks including curated ingestion, validation, end-to-end lineage tracking, access controls, and data quality monitoring to ensure the platform is a trusted, enterprise-approved data source
• Drive cloud-native architecture decisions on GCP and ensure cost-effective, scalable, and secure platform operations
• Ensure the platform provides high-quality, contextualized data that supports knowledge discovery, semantic search, entity extraction, process mining, and GenAI applications

Technical Director – Process Intelligence Data Platform

• Define and govern enterprise-wide data architecture
• Collaborate with cross-functional teams for requirements analysis
• Mentor junior architects in their professional growth
• Ensure integration of systems and technologies
• Create high-level and low-level data model diagrams
• Advise AI and ML teams on semantic modeling

Data Architect

• Design, build and test end to end data pipeline including data ingestion (streaming, events, and batch), data integration, data curation
• Design, develop, and deploy scalable data pipelines and ETL processes on cloud-based infrastructure using Azure, Snowflake, DBT, Airflow, Cosmos DB
• Define and implement automation of jobs and testing
• Optimize the data pipeline to support workloads and use cases
• Support mission critical applications and near real time data needs from the data platform
• Address data issues, environment issues, performing impact analysis, root cause analysis, corrective, adaptive and perfective maintenance
• Implement data models, transformations, and schema designs to support analytical and reporting needs
• Optimize and tune Snowflake performance, including query optimization and resource management

Senior Snowflake Data Engineer

• Responsible for the design, development, testing, and implementation of the new generation of data movement solutions for the Bank using state of the art technologies to optimize effectiveness of Provident Bank’s data warehouse.
• Design, develop, test, and implement ETL/ELT data movement jobs based on business requirements to feed data into Provident’s data warehouse and the various downstream bank applications.
• Provide technical guidance on data movement related issues and ensure optimal design for all aspects of the data warehouse.
• Perform data analysis and sourcing to target mappings and verify accuracy of data within the Bank’s data warehouse.
• Identify, design and implement process improvements (i.e. automate manual processes and optimize data delivery) to optimize the quality of data for the firm’s business intelligence team.
• Implement, maintain, and extend best practices and assets in line with the market standards and guidelines to best ensure reliability of the Bank’s data.

Lead Data Engineer, Multiple Positions Available

• Leads a team of data engineers/managers or works in individual capacity as a principal data engineer
• Responsible for providing direction in the life cycle of the solution and manage complex program with impact on business
• Implement data project according to the line of business and/or function data strategy and road map
• Work closely with architects and senior engineers to develop the best possible technical design and approach that meets to business goals
• Instill best practices for software development and documentation, assure designs meet requirements, and deliver high quality work on tight schedules
• Project management: prioritization, planning of projects and features, stakeholder management and tracking of external commitments
• Support the sharing of knowledge and best practices across the enterprise
• Hire and retain world class talent to deliver data platform projects
• Develop and nurture team of senior data engineers and/or managers

AVP II, Data Platforms

• Support infrastructure and cloud engineering initiatives by working with teams to
• Support the development and maintenance of data pipelines used for analytics, reporting, and feature engineering
• Assist with building and testing data pipelines and preparing them for deployment into production environments
• Support data transformation and integration activities, including basic ETL (extract, transform, load) processes
• Contribute to building and validating model feature jobs used for advanced analytics and data science use cases
• Assist in developing and maintaining data validation and data quality processes to ensure accuracy and reliability
• Analyze data and system outputs to identify trends, issues, or opportunities for improvement
• Support data modeling and data integration efforts, including maintaining data environments used for reporting and analytics
• Collaborate with Data Engineers, Data Scientists, and other stakeholders to support data-related initiatives
• Assist in troubleshooting data pipeline issues and support resolution efforts

Engineering Associate – Data Engineering, Analytics

• Design, build, and maintain scalable data pipelines using PySpark, Python, and Spark SQL
• Develop and optimize ETL/ELT workflows on Databricks using Delta Lake
• Implement Lakehouse architecture (Bronze/Silver/Gold layers) for enterprise data platforms
• Build and manage Databricks Jobs, Workflows, and Notebooks for batch and streaming workloads
• Ensure data quality using unit testing, validation frameworks, and automated checks

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