Top Senior Data Engineer Jobs

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Build and enhance Databricks-based platform capabilities and reusable data products for quantitative researchers and analysts. Develop and operate pipelines integrating heterogeneous data sources, while improving governance, automation, observability, security, and operational excellence. Define target-state platform architecture, evaluate emerging technologies, and use infrastructure as code, CI/CD, Apache Airflow, and AI-assisted development tools to accelerate prototypes into production.

Lead data engineering initiatives for pharmaceutical and biotech clients by building cloud-based data pipelines, warehouses, processing systems, analytics, and visualization tools. Responsibilities include analyzing business requirements, transforming big data, identifying insights, advising on standards and best practices, estimating engineering efforts, collaborating with stakeholders and leadership, and supporting growth of the data science business.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to deploy, monitor, and operate data solutions, convert diverse data formats, and support analytics, reporting, and real-time streaming use cases. Follow version control practices and meet security and clearance requirements.

Design, build, and maintain scalable data pipelines and platforms for analytics. Develop scripts to parse and convert structured and unstructured data, implement ETL/ELT workflows, collaborate with cross-functional teams, use version control, and support platform scaling, monitoring, and operations on mission-driven projects.

Lead design, development, and operationalization of enterprise-scale data applications and pipelines. Build scalable ETL/ELT workflows for structured and unstructured data, drive data architecture and migration efforts, optimize Informatica ETL, work with relational and cloud data stores, support CI/CD and DevOps, and ensure monitored, reliable data platforms to enable analytics and mission outcomes.

The Senior Data Engineer will build data pipelines and infrastructure, modernize legacy systems, and ensure data reliability for analytics at ABCorp.

Build and own data pipelines, models, quality checks, dashboards, and visualizations for manufacturing and test data. Partner with optical, manufacturing, and quality engineers to create reliable data solutions, improve pipeline performance and cost, and support engineering and leadership decisions. Establish software engineering practices for data systems, guide technical direction across distributed sites, and mentor other engineers.

Designs, builds, and operates production ETL and ELT pipelines, data models, validation, reconciliation, monitoring, and documentation. Replaces legacy processes with tested, version-controlled solutions; supports an Amazon Aurora migration; and prepares reliable datasets for reporting, analytics, and AI initiatives. Partners with engineering, architecture, reliability, database, and business teams, translates ambiguous requirements into technical solutions, makes independent architectural decisions, and mentors other engineers.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

Design, develop, and maintain batch and streaming data pipelines and ETL for large unstructured datasets. Collaborate with developers, data scientists, and analysts to build data management systems, containerize and deploy services (Docker/Kubernetes), and implement CI/CD, DevOps, and monitoring on Linux/AWS infrastructure for intelligence community missions.

Design, build, and maintain scalable relational databases, data warehouses, and integration workflows. Optimize SQL performance, ensure data integrity/security, support reporting/analytics, and collaborate with business stakeholders for timely, compliant reporting and continuity efforts.

The description combines recruitment management, event planning, business development consulting, and iOS application development. Responsibilities include developing recruitment strategies, training recruiters, maintaining candidate databases, coordinating events, managing vendors and client relationships, developing sales pipelines, and designing, testing, updating, and maintaining iOS applications.

The description is inconsistent with the title and primarily covers recruitment management, event planning, business development, and iOS application development. Responsibilities include developing recruitment strategies, managing recruiters and candidate databases, coordinating events and vendors, creating business development plans, managing client relationships, and designing, testing, and maintaining iOS applications.

Design and build scalable data engineering and AI agent governance capabilities across major cloud AI platforms. Develop ingestion, processing, ETL, observability, security, lifecycle management, and identity governance features. Evaluate emerging AI agent frameworks and interoperability standards, troubleshoot customer issues, document architecture and processes, and collaborate with engineering, security, compliance, product, and support teams. The role also involves improving distributed data systems, mentoring engineers, and leading technical initiatives.

Designs and maintains pipelines migrating financial data from legacy Oracle databases to Oracle ERP. Develops SQL and PL/SQL transformations, backend integrations, data mappings, reconciliation, testing, monitoring, troubleshooting, and documentation. Partners with accounting, business, and technical stakeholders to resolve issues and support large-scale financial transformation. The role also involves AI tool adoption, code reviews, solution design, deployment activities, governance, communication, mentoring, and project oversight.

Leads architecture and engineering standards for Curaleaf’s data and analytics ecosystem. Designs scalable Snowflake environments, ingestion and transformation pipelines, data models, semantic layers, governance, testing, CI/CD, security, and cost optimization. Partners across engineering, analytics, data science, governance, and business teams to build reusable data products, resolve incidents, improve platform reliability, and provide technical direction and mentoring without direct people management.

Lead customer integrations by building warehouse-native or managed ClickHouse pipelines, construct semantic metadata layers, canonicalize source data into unified growth models, and act as the technical lead for enterprise deployments while partnering with AI discovery agents to accelerate onboarding and mapping.

Design, develop, and monitor large-scale data pipelines and cloud-native data applications; collaborate with consulting teams and clients; apply best practices (TDD, CI/CD); build and operationalize ML-ready data platforms; produce documentation and runbooks; mentor junior engineers and contribute to proposals and analytics assets.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Build and scale core data infrastructure and foundational datasets. Design, develop, and maintain scalable data pipelines for analytics, ML, experimentation, and product use cases. Implement data quality, audits, alerting, and anomaly detection. Partner with product, engineering, and business stakeholders and help establish long-term, scalable data architecture and tooling.

Integrate operational data into enterprise data warehouses; design, optimize, and support SSIS ETL pipelines; develop SSRS and Power BI reporting solutions and reusable data models; deliver data migrations and self-service extracts. Design production AI/ML solutions using Python, OCR, AWS Bedrock, and Claude, including multi-agent LLM implementations. Partner with stakeholders, lead code reviews, mentor developers, and support production BI deployments.

The Senior Data Engineer will architect and enhance data infrastructure, create dependable data pipelines, oversee cloud-based data warehouses, and mentor engineers to enable scalable solutions for analytics and machine learning.

Designs, builds, and optimizes scalable AWS and Snowflake data pipelines, infrastructure, data models, semantic layers, and data quality frameworks. Analyzes Medicare, Medicaid, and CMS repository data to support analytics and reporting. Responsibilities include workflow monitoring, automation, governance, security, performance optimization, stakeholder collaboration, and Agile delivery using modern DevOps and software engineering practices.