Top 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.

Design, build, maintain, and optimize data infrastructure (databases, data lakes, warehouses) and ETL/ELT pipelines. Ensure data quality, security, performance, and compliance. Collaborate with analysts and stakeholders, document policies, and support monitoring, backup, and recovery strategies.

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.

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.

Set up test, preproduction and developer environments on physical servers; act as SQL Server DBA including security auditing; develop Big Data solutions and programs; perform user acceptance and integration testing. Requires experience with DTS/SSIS/SSRS, T-SQL, C#, PowerShell, Hive/Hadoop, and Microsoft SQL Server 2000 certification.

Build and own the core data models and pipelines connecting product usage, accounts, revenue, billing, and customer health. Create trusted metrics, dashboards, and self-serve reporting. Partner with Product, Engineering, and GTM teams to improve data quality, design AI-enabled workflows and agents, and support leadership with accurate visibility into adoption, pipeline, retention, expansion, and revenue.

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.

The Data Engineer will construct data infrastructure, develop machine learning workflows, maintain data pipelines, integrate software components, and collaborate across teams to enable data-driven decisions.

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.

Lead end-to-end data integrations with utility customers: scope requirements, map and transform datasets, run and monitor pipelines, triage issues, and productize repeatable tooling to speed implementations.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

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.

Develop and optimize SAP Data Services ETL/ELT workflows integrating SAP and non-SAP systems. Create source-to-target mappings, extraction routines, data cleansing and validation processes, profiling routines, and business-rule transformations. Monitor and troubleshoot job execution and performance. The role requires strong SAP data-model knowledge, advanced SQL, HANA optimization, SAP BW/4HANA, SAP MDG, Information Steward, Unix/Linux shell scripting, and active DoD Secret clearance.

Build, operate, and enhance Banc of California’s Azure- and Snowflake-based data platform. Develop batch, CDC, and streaming ingestion pipelines; optimize Snowflake performance; support dbt workloads; implement CI/CD, DataOps, monitoring, automation, and disaster recovery. Provide Tier 2 production support and administer tools including Power BI, Purview, and Alteryx. Collaborate with data engineering, architecture, BI, and business teams to deliver secure, reliable, governed data solutions.

Own the architecture, technical direction, reliability, and evolution of a shared cloud data platform. Lead hands-on development of streaming and batch pipelines using infrastructure-as-code, build governed analytical data products, and establish data contracts, quality, security, lineage, and observability standards. Serve as the final technical escalation point, mentor engineers, lead design and code reviews, promote responsible AI-assisted coding, respond to incidents, and communicate technical tradeoffs across stakeholders.

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.