Data Engineer

• Design and ship production pipelines on Databricks, using a medallion architecture grounded in Data Vault 2.0 modeling patterns.
• Build and harden the curated and presentation layers—driving donor lifetime value, retention, lapse risk, and campaign ROI.
• Resolve identity across products to compute values on one trustworthy record.
• Move toward near-real-time data and partner with architects on Change Data Capture.
• Extend existing tracing and AI lifecycle tooling into ETL.
• Partner with AI and product engineers to ensure data is properly shaped for applications.
• Use AI tools in daily development tasks.
• Raise the bar on engineering standards, addressing testing, idempotency, documentation, and security.

Senior Data Engineer

• Design and maintain enterprise reporting architecture supporting program management, operational reporting, and executive dashboards.
• Develop scalable data models, reporting frameworks, and business intelligence solutions using industry best practices.
• Collaborate with business stakeholders to define reporting requirements, key performance indicators (KPIs), and data visualization strategies.
• Design and optimize SQL queries, stored procedures, and data pipelines supporting enterprise reporting initiatives.
• Develop executive dashboards and interactive reports using Power BI, Tableau, or similar business intelligence platforms.
• Ensure reporting accuracy through data validation, quality assurance, and data governance processes.
• Partner with Data Analysts, Business Analysts, and technical teams to identify new reporting opportunities and improve data accessibility.
• Maintain technical documentation, data dictionaries, reporting standards, and architectural diagrams.
• Support executive governance meetings by providing reporting solutions and analytical insights that inform strategic decision-making.
• Recommend continuous improvements to reporting architecture, data integration processes, and business intelligence capabilities.

Data / Reporting Architect – Federal Health

• The ADABAS Data Engineer is responsible for designing, developing, maintaining, and optimizing data solutions built on the ADABAS platform.
• Analyze business and technical requirements and translate them into database designs and data models.
• Develop and optimize ADABAS queries, utilities, and data extraction processes.
• Work with Natural, COBOL, JCL, and other mainframe technologies to support data processing requirements.
• Perform data migration, conversion, and integration activities between ADABAS and modern data platforms.
• Monitor database performance and implement tuning strategies to improve efficiency and reliability.
• Ensure data integrity, backup, recovery, and security procedures are maintained.
• Troubleshoot and resolve database-related issues, incidents, and performance bottlenecks.
• Collaborate with application developers, architects, business analysts, and infrastructure teams.
• Support modernization efforts involving API integration, cloud migration, and data warehousing initiatives.

ADABAS Data Engineer

• The Principal Translational Knowledge Architect & Graph Lead will be responsible for designing and implementing the semantic and knowledge architecture that enables AI-driven reasoning across the drug discovery and development lifecycle.
• This role will serve as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation.
• Working at the intersection of translational science, patient safety, biomedical informatics, and artificial intelligence, this individual will help establish the semantic foundation required to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework.
• The successful candidate will partner closely with scientists, safety experts, data scientists, AI engineers, and platform teams to create knowledge assets that support GraphRAG, agentic AI, scientific reasoning, and next-generation translational intelligence capabilities.

Principal Scientist, Data Science – Translational Knowledge Engineering

• Designing and implementing AI systems to transform raw data into actionable insights
• Developing scalable machine learning models to support client decision-making processes
• Collaborating with cross-functional teams to integrate AI solutions into existing platforms
• Conducting data wrangling and preprocessing to prepare datasets for analysis
• Applying advanced algorithms to enhance the performance of AI models
• Building and deploying software systems that leverage AI and machine learning technologies
• Analyzing complex data sets to identify patterns and trends that inform business strategies
• Supporting client engagements by delivering quality work and adapting to diverse project requirements
• Engaging in continuous learning to deepen technical skills and knowledge in AI engineering
• Upholding professional and technical standards while contributing to team success

AI Engineer, AI Experienced Associate

• Lead the design, development, and support of party reference data applications, APIs, and data services.
• Partner with business stakeholders, architects, and engineering teams to deliver reference data solutions supporting client, counterparty, issuer, and legal entity domains.
• Develop and maintain cloud-native data pipelines utilizing AWS services such as Glue, Lambda, EventBridge, S3, RDS, and Kafka/MSK.
• Support the modernization of legacy reference data platforms through migration to scalable cloud-based solutions.
• Integrate third-party reference data sources including Dun & Bradstreet, S&P Global, and other data providers to support enrichment, validation, and compliance requirements.
• Develop and support APIs and data services consumed by CRM, compliance, reporting, operations, and trading platforms.
• Implement data quality controls, validation rules, matching and deduplication processes, survivorship logic, and lineage tracking capabilities.
• Collaborate with data governance and architecture teams to support reference data standards, metadata management, and best practices.
• Participate in Agile planning, backlog refinement, estimation, sprint execution, and delivery activities using Jira.
• Provide technical leadership and mentorship to junior engineers and project team members.

Lead Data Engineer

• Serve as the go-to subject matter expert on Salesforce Agentforce for internal teams and enterprise customers.
• Advise definitively on optimal use cases, implementation patterns.
• Act as an escalation point for critical or complex Agentforce project challenges.
• Lead the technical design and hands-on development of complex Agentforce solutions.
• Partner with customers and account teams to assess business challenges, identify high-impact Agentforce use cases.
• Lead and contribute to mentorship and enablement programs.
• Maintain a high degree of expertise on the rapidly evolving Agentforce product roadmap.
• Provide critical “Voice of the Customer” feedback to Product and Engineering organizations.
• Actively partner with GTM counterparts during pre-sales activities.

Senior AI and Data Architect

• Design and lead AI-based/digital solutions and services, selecting optimal architectures aligned with the company’s cloud, data, and security standards.
• Translate business requirements into implementable architectures and solutions across data platforms, AI models, application services, orchestration frameworks, and extensibility layers.
• Design, develop, test, deploy, support and enhance new or off-the-shelf secure AI and ML models, including Retrieval-Augmented Generation (RAG), tailored to domain-specific deliverables and company initiatives.
• Build the Generative AI platforms, systems and infrastructure and apply general knowledge of software development and infrastructure as code practices.
• Automate engineering and business processes using scripting languages and modern automation platforms, including Python and cloud-based AI services from Microsoft (e.g., Copilot Studio, Azure Foundry, etc.).
• Design and implement user interfaces, data visualizations, and other interaction layers as needed to support AI-enabled workflows and decision-making.
• Develop Python scripts for data analysis and automation of engineering analysis.
• Design, develop, test, deploy, support, and enhance production-grade applications and services that enable AI and data-driven workflows.
• Integrate front-end components with back-end APIs and services for seamless data interactions.
• Ensure UI/UX best practices to improve user experience and workflow efficiency.
• Develop, manage, and maintain relational database applications for data storage and retrieval, ensuring performance, scalability, and security.
• Implement and maintain RESTful APIs and backend services to support frontend applications.
• Lead solution architecture for priority AI, generative AI, and agent-based use cases, including hands-on technical design, decision-making, and architecture reviews.
• Identify technical, security, data, and delivery risks associated with AI solutions and implement appropriate mitigation strategies.
• Embed Responsible AI principles into solution designs, including privacy, security, transparency, and model risk management.
• Maintain a strong awareness of the latest trends in AI/ML and their applications to engineering consulting and related industries, ensuring that the company remains at the forefront of technological advancements.
• Provide training and support to internal teams on AI/ML technologies and best practices, fostering a culture of continuous learning and innovation.
• Maintain comprehensive documentation for all AI/ML projects, including design specifications, solution diagrams, implementation plans, and user guides.
• Responsible for coordinating transportation to and from the office; and local travel or driving to project sites as needed. Drive personal, company, and rental vehicles to client or company project or office sites, and other business locations, as needed.

Software/Data Scientist/Engineer

• As an Operations Engineer - Data Center (SysOps), you will be part of a diverse team that is responsible for supporting mission-critical hardware and infrastructure as well as maintaining the day-to-day operations of our data centers.
• Match Group hosts several sites like Match, Plenty Of Fish, BLK and many others from our physical data centers.
• Through continued innovations, we are able to deliver a truly amazing experience to millions of singles that use our platform.
• Your role will require onsite presence in our data centers in Dallas, TX and surrounding areas, but also accommodates a hybrid schedule.
• Travel is also required while supporting multiple locations 10% of the time.

Operations Engineer – Data Center, SysOps

• Shape the data architecture and data models for a large federal data-lake-plus-warehouse platform on Amazon Web Services (AWS)
• Clean data models and schemas across the lake and the warehouse (Amazon Redshift, S3, and relational databases)
• Design the models and schemas that new and migrated sources land in
• Restructure the existing ecosystem to cut redundancy and protect integrity and accessibility
• Bring new capabilities, tools, and lines of business to the firm

Data Architect

• Build pipelines that move a federal agency's data from source to warehouse on AWS
• Ingest, process, store, and keep data clean and trustworthy at scale
• Create a data lake and manage the design into Redshift with event orchestration and quality validation

• You will own and evolve our data pipelines on GCP — building new ones, hardening existing ones, improving data quality, and making clean, trustworthy data available across the organisation.
• Designing ELT/ETL processes on BigQuery and ClickHouse, building real-time pipelines on Pub/Sub and Kafka with Dataflow (and where it fits, Flink/Spark).
• Orchestrating workflows with Airflow, and ensuring data is properly cleaned, modelled, and served for analytics, ML training, and online inference.
• Partnering with ML engineers on feature pipelines, monitoring data drift, and keeping models well-fed and retrained as needed.
• Consuming and building REST APIs, integrate with third-party SaaS sources, and treating infrastructure as code.
• Collaborating closely with Infrastructure, Software Engineering, Product, and ML/AI engineers.
• Helping drive the migration off Snowflake onto GCP-native stack — and retire shadow pipelines along the way.
• Owning the orchestration layer in Airflow, including SLAs, retries, and data quality gates.
• Modeling data for analytics and for ML — including feature pipelines that serve both training and low-latency online inference.
• Capturing requirements from stakeholders and translating them into pragmatic, well-scoped data products.
• Continuously improving data quality, reliability, observability, and cost efficiency.
• Identifying new data sources worth acquiring and integrating them cleanly.

• Own the technical strategy and roadmap - Own the vision, architecture, and roadmap for the CodaMetrix data platform, ensuring scalability, reliability, regulatory alignment, and operational excellence. Lead key technology decisions, disaster recovery design, architecture reviews, and data engineering standards across teams.
• Platform Engineering at Scale - Design, build, and maintain scalable streaming and batch data platforms Databricks using PySpark, Unity Catalog, Delta Lake, and Structured Streaming. Own Terraform-based infrastructure across environments, including jobs, catalogs, schemas, permissions, compute policies, volumes, and external locations. Build Jenkins CI/CD workflows for automated testing, tagging, and deployments, while optimizing training pipelines, materialized views, retention policies, and production performance.
• Data Governance & Security - Implement and evolve least-privilege access controls across Databricks using Unity Catalog grants, YAML-driven group policies, and schema-level restrictions. Ensure HIPAA and SOC 2 compliance through PHI masking, audit logging, environment-level data segmentation, user provisioning, compute policies, and cost attribution.
• Cost Optimization & Operational Excellence - Drive platform cost reduction through compute policy tuning, serverless optimization, reserved pools, materialized view improvements, and remediation of underutilized resources. Monitor Databricks/AWS spend (using tools like CloudZero and AWS Cost Explorer), own cost attribution and budgeting, resolve production incidents, and maintain runbooks to ensure platform SLAs for uptime and performance.
• Cross-Functional Enablement & Mentorship - Enable ML, BI, Analytics, DevOps, and customer success and implementation teams with training data pipelines, model-ready datasets, feature store architecture, optimized views, dashboards, Tableau refreshes, infrastructure changes, and tenant onboarding. Mentor engineers through code and design reviews, establish quality standards, and serve as a subject matter expert for data platform engineering across the organization.

Principal Data Engineer

• Design the lakehouse: Apache Iceberg (or similar technology) on object storage, a catalog for table management and per-bank isolation, dbt models, and a query engine
• Build secure, least-privilege ingestion from bank systems — log-based CDC where permitted, with query-based and batch/SFTP fallbacks, plus an in-bank collector pattern
• Own data modeling for the semantic and metric layer (deposits, concentration, uninsured exposure, asset quality, and peer groups)
• Handle schema drift, data quality, and reconciliation; make ingestion observable and recoverable
• Partner with the AI/ML team on the structured-query path and with Security on PII classification at landing, in alignment with regulatory data-handling requirements
• Document data lineage, transformation logic, and access controls to support audit and exam readiness
• Define and enforce data contracts, quality thresholds, and alerting for pipeline failures

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand, improve service SLAs, and minimize gaps
• Serve as the escalation point for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health and streamline tasks
• Monitor backup systems and performance, identifying and troubleshooting issues
• Configure access using a least-privileged approach, audit, and ensure adherence to policies
• Communicate upgrade and rollback plans, and execute upgrades
• Build lasting trust with clients

Data Protection Engineer

• Partner with business stakeholders, Product Owners, SMEs, analysts, and development teams to understand business processes, data requirements, source systems, data mappings, and data consumption needs.
• Translate business requirements, processes, and rules into logical and physical data models, source-to-target mappings, and data integration specifications.
• Define and implement test data strategies that support functional, regression, integration, performance, automation, and modernization testing needs.
• Design and govern reusable test data patterns, test data provisioning approaches, masking strategies, synthetic data solutions, and environment refresh processes.
• Demonstrate strong understanding of data modeling principles, including entities, attributes, relationships, schemas, primary and foreign keys, indexes, constraints, and overall database design.
• Read, interpret, and analyze Entity Relationship Diagrams, database schemas, data dictionaries, and end-to-end data flow diagrams to identify data dependencies and business impact.
• Perform data discovery, analysis, and data mining across multiple databases, tables, files, and source systems using SQL, Easytrieve, and other data query and extraction tools.
• Validate data quality, integrity, consistency, and accuracy by analyzing data relationships, transformation logic, reconciliation results, and business rules across systems.
• Develop an understanding of data movement and integration patterns, including real-time and batch processing, event-driven architectures, APIs, messaging queues, data pipelines, and file-based interfaces.
• Collaborate with architecture, development, testing, and operational teams to support data analysis, problem resolution, impact assessments, test automation enablement, and system modernization initiatives.
• Ensure test data solutions comply with data privacy, data protection, retention, classification, and regulatory requirements for sensitive customer and financial data.
• Document data models, test data design patterns, source-to-target mappings, provisioning processes, standards, and best practices for repeatable enterprise use.

• Leverage data and analytics to provide strategic insights
• Develop and implement innovative solutions to optimize business performance
• Build and maintain data pipelines and architectures
• Implement data visualization tools to provide actionable insights
• Collaborate with stakeholders to identify and address data challenges
• Coach and motivate teams to solve complex problems
• Validate outcomes with clients and act on feedback

Senior Data Engineer – Senior Manager

• Provide consultation and data-strategy support for IS initiatives.
• Run EA IS Data Strategy Office Hours and provide guidance on data challenges.
• Partners closely with the EDP team—understand their roadmap and share relevant updates with IS EA architects.
• Design and develop information/data models (enterprise, conceptual, logical, and physical) for Industrial Systems; manage and maintain models in a Power Designer repository.
• Map applications to the conceptual data model, business processes, and business capabilities.
• Support product-team intake: translate use cases into data requirements and provide expert architecture guidance and designs.
• Lead discovery, onboarding, and analysis of sources into Industrial Systems Data Platform (ISDP); build deep understanding of the data landscape and enable integration of diverse sources into ISDP.
• Lead conceptual discussions to understand the data landscape; identify and evaluate candidate data sources; maintain data models and data dictionaries for identified source systems; and develop canonical domain models.
• Profile source-system data; define data-quality requirements, dimensions, and metrics; and guide monitoring, measurement, reporting, and issue remediation.
• Establish data-architecture principles, standards, best practices, and guidelines for the data platform (catalog, lineage, observability, security, interoperability).
• Perform proof-of-concepts and guide product teams in designing and developing solutions using GCP data-engineering technologies and tools.

Data, Information Architect

• Lead the design, development, and support of party reference data applications and services.
• Partner with architects and business stakeholders to implement data solutions supporting client, counterparty, issuer, and legal entity data domains.
• Build and maintain cloud-native data pipelines utilizing AWS services including Glue, Lambda, EventBridge, S3, RDS, and Kafka/MSK.
• Support modernization initiatives by migrating legacy reference data platforms to scalable cloud-based architectures.
• Integrate and manage third-party reference data providers such as Dun & Bradstreet, S&P Global, and other enrichment sources.
• Develop and maintain APIs and data services supporting compliance, CRM, operations, reporting, and trading systems.
• Implement data quality controls, validation frameworks, deduplication processes, and lineage tracking capabilities.
• Collaborate with data governance and architecture teams to support reference data standards and best practices.
• Participate in Agile ceremonies, backlog refinement, estimation, and technical delivery planning using Jira.
• Mentor junior engineers and provide technical guidance across project teams.
• Support platform monitoring, resiliency, performance tuning, observability, and operational excellence initiatives.

• Develops, documents, tests, debugs, and maintains data; provides expertise and familiarity in software tools, frameworks and best practices in secure software engineering.
• Collaborates effectively with other members of the team to deliver on the team’s backlog.
• Participates in the discussion and implementation around complex capabilities.
• Develops complex features and capabilities to enhance the Early Warning core product offerings.
• Manages source code using Git.
• Receives, reviews and approves pull requests ensuring that code quality remains high.
• Processes both real time stream data and file based data through ETL frameworks and persist the data into the data store.
• Builds aggregations on the data to enable provisioning of data to the AI/ML models as input and also deliver them to the customers.
• Document complex details and decisions in Confluence.
• Builds automated tests alongside sprint work that is integrated into the CI/CD pipeline, considering environment variables, and provides recommendations.
• Able to troubleshoot complex bugs that may arise during development.
• Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

Data Engineer jobs in United States

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Senior Data Engineer

Data / Reporting Architect – Federal Health

ADABAS Data Engineer

Principal Scientist, Data Science – Translational Knowledge Engineering

AI Engineer, AI Experienced Associate

Lead Data Engineer

Senior AI and Data Architect

Software/Data Scientist/Engineer

Operations Engineer – Data Center, SysOps

Data Architect

Data Engineer

Senior Data Engineer

Principal Data Engineer

Lead Data Engineer

Data Protection Engineer

Data Architect

Senior Data Engineer – Senior Manager

Data, Information Architect

Lead Data Engineer

Senior Software Engineer – Data Engineering