Top Data Engineer Jobs

Designs, develops, and optimizes Databricks and Spark data pipelines integrating Oracle EBS and ERP Cloud data. Builds ETL/ELT jobs, data models, validation processes, and reporting solutions across financial, purchasing, project, inventory, and HR modules. Ensures data quality, lineage, reconciliation, security, governance, and compliance while partnering with Oracle SMEs and documenting solutions for federal review and audits.

Develops and supports secure data integrations, applications, and workflows using Microsoft Dynamics CRM and Power Platform. Responsibilities include requirements gathering, solution design, implementation, ALM, systems integration, monitoring, troubleshooting, reporting, plug-in administration, documentation, vendor coordination, and data reconciliation. The role also creates reusable technical solutions, supports customer-facing applications, and collaborates with business analysts to improve customer insights and revenue-generating services.

Build and maintain Python and Databricks workflows for data cataloging, API ingestion, and transformation across varied sources and file types. Develop authentication workbooks and Power Apps using Microsoft Power Platform. Gather client requirements, translate technical findings for diverse audiences, and deliver mission-focused data solutions. The role also supports data engineering, metadata management, Agile practices, and federal client initiatives.

Designs, builds, and optimizes SAP HANA-native data models and analytical objects for S/4HANA, BW/4HANA, and standalone HANA platforms. Translates business requirements into performant calculation views, CDS views, AMDPs, and SQLScript solutions; diagnoses and resolves performance issues; applies HANA tuning methodologies; supports replication, security, analytics modeling, migrations, and upgrades. Requires strong SAP HANA architecture, SQL, troubleshooting, communication, and documentation skills.

Build and optimize scalable data platforms, database architectures, and ETL/ELT pipelines using SQL Server, Azure Data Factory, Azure services, and Python. Integrate healthcare and enterprise system data, perform profiling and transformation, implement master data management, establish lineage and monitoring, and support AI/LLM-based document extraction. Collaborate with stakeholders and engineers to deliver reliable, well-tested, documented data solutions.

Designs and operates a scalable data platform supporting entity resolution, knowledge graphs, real-time risk assessment, APIs, and batch and streaming pipelines. Owns data reliability, observability, governance, performance, and platform architecture while collaborating with data science, analytics, product, and engineering teams. The role requires graph databases, cloud-native AWS services, modern data stack technologies, DevOps, and data-as-a-service experience.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Lead data engineering initiatives supporting analytics, machine learning, and business intelligence teams. Design complex data warehouse and data lake models, manage large-scale SQL and Python ETL/ELT pipelines, maintain data quality and lifecycle standards, and translate strategic priorities into actionable projects. Provide technical leadership, mentorship, architectural guidance, project planning, and cross-functional collaboration with product, infrastructure, and architecture teams.

Develops and maintains data applications, scalable ETL/ELT workflows, and pipelines for structured and unstructured data. Designs, builds, and manages analytics solutions while collaborating with analysts, developers, data engineers, and data consumers in an agile environment. The role requires programming and querying expertise, data processing experience, a bachelor's degree, and Secret clearance.

Develop and maintain data engineering solutions using Informatica Data Management Cloud, Control-M, Snowflake, and SQL. Responsibilities include sourcing, transforming, cleansing, ingesting, analyzing, and visualizing data; building automated workflows and scalable ETL processes; supporting data architecture and deployments; and delivering reusable code for large datasets. The role requires collaboration with technical and business partners, troubleshooting, mentoring, and managing multiple priorities.

Build and maintain scalable data pipelines and platforms for structured and unstructured data. Develop ETL and ELT workflows, convert data into usable formats, and support platform monitoring and operations. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while applying analytical exploration and data examination to mission-driven client projects.

Leads the design, development, governance, and operational support of enterprise data engineering solutions for Fresh Meats analytics. Builds cloud-based data lakes, pipelines, models, integrations, and reporting-ready datasets; implements security, data quality, lineage, observability, and governance controls; supports production incidents and releases; and partners with business, platform, and governance teams. Mentors engineers and analysts while ensuring scalable, documented, secure, and reliable data products.

Build and maintain scalable data platforms, warehouses, lakehouses, ETL pipelines, and Spark-based transformations using Databricks, Snowflake, and AWS. Develop tracking architecture, data models, governance, lineage, monitoring, and optimization frameworks. Integrate machine learning and AI-assisted development practices, support analytics and KPI dashboards, and provide data access and insights across departments. Mentor engineers and analysts while coordinating with software engineering, product, DevOps, DBA, and security teams.

Hands-on principal data engineer defining enterprise-scale data architecture, building and optimizing data pipelines/integrations for analytics and ML, deploying production code, mentoring engineers, and partnering with product and ML teams to transform and analyze data for actionable insights.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Develops and delivers operational and analytical data solutions within an enterprise data warehouse and business intelligence environment. Responsibilities include building data pipelines, creating logical data models and mappings, supporting requirements through implementation, testing and maintenance, partnering with analysts and IT teams, and investigating data quality issues. The role requires experience with programming, cloud platforms, data warehouses, databases, ETL tools, and Windows/Linux systems.

Design, develop, test, deploy, and optimize high-throughput streaming, event-driven, and batch data pipelines using Databricks, Scala/PySpark, and cloud infrastructure. Build data lakehouse solutions, troubleshoot DevOps and Azure issues, perform root-cause and impact analysis, and support the full software development lifecycle. Collaborate with distributed stakeholders, drive technical improvements through proofs of concept, and maintain scalable data integration and curation solutions.

Design and scale event-driven data platforms using Apache Kafka and Temporal. Build batch and streaming ETL/ELT pipelines with Python, SQL, and Spark; develop data schemas, validation, contracts, and quality controls; optimize warehouse and lakehouse models; and implement reliable, observable workflows. Collaborate with software, machine learning, and analytics teams on distributed systems, CI/CD, and production data governance.

Designs and maintains operational data infrastructure for smart manufacturing, including pipelines, data warehouses, ETL/ELT processes, time-series data systems, integrations, governance, and trusted datasets. Uses SQL and Python to support reporting, analytics, automation, dashboards, and operational decision-making. Partners across manufacturing and enterprise systems, investigates complex data challenges, evaluates emerging technologies, and supports advanced analytics and digital transformation initiatives.

Design, build, and maintain scalable cloud data pipelines, datasets, ingestion frameworks, and automation for cloud cost visibility and usage insights. The role uses AWS, Snowflake, SQL, Python, orchestration tools, Terraform, and CI/CD while ensuring data quality, auditability, reliability, and performance. Responsibilities include query and cost optimization, REST API integrations, production troubleshooting, and collaboration with Product, Engineering, and Finance stakeholders. This is a remote LATAM contract requiring a U.S. EST or CST work schedule through March 2027.

Owns dbt-based transformation, dimensional modeling, and analytical data infrastructure across finance and land initiatives. Builds scalable AWS data pipelines, optimizes Snowflake storage and retrieval, develops SQL and Python solutions, and collaborates with stakeholders, data scientists, and BI engineers. Responsibilities include ETL, data warehousing, reporting infrastructure, cloud resource management, workflow automation, data security, CI/CD, and support for emerging agentic data products and MCP-based access.