Top Data Engineer Jobs

Lead design and implementation of scalable GCP-based data pipelines and semantic layers for enterprise healthcare analytics. Drive ingestion automation, BigQuery optimization, ETL/ELT orchestration, observability, data quality, and metadata management. Provide technical leadership, mentor engineers, and collaborate with architects and stakeholders to deliver production-ready cloud data solutions.

Design, build, and maintain scalable cloud and hybrid data platforms, ETL/ELT pipelines, and data models. Implement CI/CD and DevOps practices, ensure data quality, security, and governance, optimize production pipelines, mentor junior engineers, and support data modernization and migration efforts for analytics and operational workloads.

Design, build, and operate large-scale batch and real-time data pipelines. Implement ingestion, transformation, orchestration, streaming, and downstream delivery using Kafka, Spark, cloud platforms, and workflow tools. Ensure reliability, scalability, performance, security, secrets management, and observability while collaborating with Agile teams to support enterprise analytics workloads.

Design, implement, and own Oracle data architecture and schemas for a cloud-native centralized data platform. Develop PL/SQL/SQL artifacts, optimize performance, enforce data standards, support ETL/CDC pipelines, ensure secure scalable database solutions, and collaborate with cross-functional teams to deliver mission-ready data products.

Own developer relations for GPU-accelerated analytical data processing: integrate NVIDIA libraries into query engines and OLAP databases, produce rigorous benchmarks, create reference integrations and guides, collaborate with engine architects and NVIDIA product teams to define APIs and roadmap, and drive adoption across open source and commercial projects.

Design and implement AI-optimized data platforms and production ETL/ELT pipelines, build semantic and analytical layers, write production-ready SQL/Python code, collaborate with stakeholders and analytics teams, and contribute to the firms data engineering practice and client delivery.

Lead and deliver AI-optimized data engineering solutions: design cloud data platforms, build ELT/ETL pipelines, implement semantic/analytic layers, write production SQL/Python/Spark code, apply AI-assisted tooling, and lead small teams on client engagements.

Lead architecture and engineering for enterprise healthcare data platforms. Design and implement scalable data warehouses, lakehouses, ETL/streaming pipelines, data governance/security, APIs, and infrastructure as code. Advise stakeholders, mentor teams, optimize performance and costs, and ensure compliance with clinical data standards (FHIR, HL7, OMOP) while delivering reliable data products for internal and external users.

Build and maintain governed, activation-ready marketing data products in Snowflake. Design canonical marketing data models, event-streaming, identity resolution, quality checks, and reverse-ETL activation for marketing, CRM, paid media and AI use-cases. Enable predictive scoring, attribution, and AI-ready context layers while partnering with cross-functional stakeholders.

Lead ETL and data architecture for AML, trade/sales surveillance, and compliance data repositories. Design and develop complex Informatica PowerCenter 9.1 mappings, enforce ETL architecture standards, tune performance, automate and maintain ETL utilities, collaborate with BAs and DBAs, provide production support, and mentor junior ETL developers.

Designs and implements large-scale data solutions using Java and open-source big data technologies (Hadoop, NoSQL, Solr, Storm, Kafka). Participates in architecture and design discussions, delivers implementations, collaborates with senior architects and offshore teams, authors technical papers, and shares knowledge across the organization while ensuring performance, scalability, and timely quality delivery.

Design and implement large-scale data processing solutions (GB/PB) using Hadoop, NoSQL, MPP and related open-source tech. Participate in architecture/design, provide implementation details to offshore teams, produce whitepapers/case studies, ensure timely quality deliveries, and share knowledge through training sessions.

Design, implement, and maintain enterprise database solutions and Big Data (Hadoop) systems. Lead database architecture, HA/DR, migrations, patching, performance tuning, security, capacity planning, monitoring, and incident recovery. Provide technical leadership, SOP documentation, and support BI/data mining activities. Role requires shift work.

The Senior Data Engineer will optimize the data platform, build scalable data pipelines, improve data access and quality, and establish data engineering discipline within the company.

Build, operate, and scale governed data pipelines and curated lakehouse datasets on Azure to support analytics, operational intelligence, and AI-enabled use cases. Deliver production-ready PySpark/SQL transformations, manage pipeline monitoring and performance, document lineage and business rules, and collaborate with stakeholders to ensure secure, governed, and reusable data products.

Design and build scalable platforms for data ingestion and storage, collaborate with hardware engineers, and define engineering processes at Sift.

Build and maintain scalable cloud-native data platforms and pipelines for analytics. Design system architecture, ingest high-volume bounded and unbounded data, optimize performance and cost, write tests and documentation, collaborate with product and EHR specialists, and adopt AI-assisted development tools to improve delivery and quality.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Lead design and implementation of scalable Azure-based data pipelines and architectures using ADF, Synapse, Spark/Databricks, and Power BI. Mentor engineering teams, ensure data security/compliance, collaborate with stakeholders, and drive adoption of best practices and new technologies.

Design, build, and maintain scalable ingestion, normalization, validation, storage, and lifecycle management of large-scale market and alternative datasets. Develop Python- and SQL-based pipelines, monitoring, and tooling to ensure research-ready, high-quality data across multiple asset classes while collaborating with researchers and infrastructure teams.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.

Design, build, and operate production-grade near-real-time data pipelines and APIs on AWS (Kinesis, Glue/Spark, Lambda, EMR). Implement IaC and CI/CD, optimize data lakes and operational models, improve observability and reliability, and participate in on-call support.

Map source data entities and create transformation rules for ETL developers to load into target systems. Work with business clients to interpret rules. SQL and Excel required; telecom billing system conversion/integration (Amdocs) is strongly preferred.

Design and build IBM DataStage ETL jobs to extract, transform, and load data into 3NF and star schemas. Implement Slowly Changing Dimensions and Change Data Capture, write strong Oracle SQL, use QualityStage and parallel processing, create source-to-target mappings, write/execute test cases, and use Unix shell scripting. Collaborate with stakeholders and maintain data warehouse ETL processes.

Design, build and deliver SAP-originated and non‑SAP data in formats consumable by non‑SAP tools and external B2B partners using SAP BO/Data Services, BW/BI. Support ETL, integration and data delivery processes.