Top Data Engineer Jobs

The Sr. Ab Initio Data Integration Engineer will maintain ETL processes, troubleshoot performance issues, and provide documentation for the data warehouse environment.

As a Lead Data Engineer, you will architect data foundations for AI applications, lead a team, optimize data pipelines, and ensure data governance.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

Design, build, and operate data services and pipelines to ingest, standardize, and deliver clinical data for product, analytics, and ML. Tune Spark workloads, enforce data quality, monitor production systems, and partner with product and engineering. Participate in on-call support.

The Senior Data Engineer will work with data tuning, big data technologies, and coordinate with technical teams and business users.

Design, build, and maintain scalable data pipelines and infrastructure on AWS, collaborating with data scientists and AI teams to support analytics and ML initiatives.

The Senior Data Engineer will work with data tuning on relational databases, utilize big data technologies, and coordinate with teams. A strong academic background and experience with large datasets are required.

As a Data Engineer, you will architect and maintain the data systems, create pipelines and integrations, and ensure data quality for analytics and reporting.

The Principal Data Engineer will lead complex data projects, design robust data pipelines, and drive architectural strategy for a scalable data ecosystem, while mentoring other engineers and ensuring the quality and usability of data solutions.

Design, build, and maintain scalable data processing pipelines and infrastructure (ETL/ELT). Implement large-scale data processing (Spark, MapReduce, Kafka), support stakeholders with analysis and reporting, ensure secure multi-region data handling, and optimize data delivery for analytics and business insights.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

Support and monitor data pipelines and curated datasets, perform exploratory analysis and model validation, develop SQL data models in Snowflake, troubleshoot ETL/ELT jobs, integrate proprietary and third-party data, validate AI-generated insights, and collaborate with stakeholders to deliver Customer 360 and leadership analytics.

Lead and deliver enterprise data programs: design Snowflake-based data solutions, data models, ETL and warehouse applications, provide architecture direction, drive technology transformation, collaborate with stakeholders, and apply advanced analytics (ML/AI/GenAI).

Develop and validate daily data feeds, reports, and applications; vet other developers' work for data integrity; collaborate with business partners; participate in Agile ceremonies; identify and improve ETL performance; maintain clear documentation.

Build and maintain dbt models and ETL/ELT pipelines on BigQuery and Daasity, connect marketing, inventory, and finance data, support KPI tracking and predictive models, deploy dashboards, and collaborate with AI and business teams to ensure clean warehouse outputs for analytics and automation.

Design and implement scalable, production-quality data solutions using cloud-based big data technologies. Develop and optimize Spark/Hive workflows, write code in Scala/Python/Java, perform performance tuning, participate in SDLC and agile processes, and collaborate to identify and execute improvements.

Design, build, and maintain scalable analytics pipelines and data models. Write efficient SQL, create dashboards and reports, monitor data quality, and partner with cross-functional teams. Leverage AI tools to accelerate analytics workflows and improve decision-making across the company.

Implement Redpoint Customer Engagement Hub and RPDM ETL, data hygiene, and identity resolution. Build data ingestion/automation, troubleshoot client implementations, collaborate with development and project teams, and support product feature identification.

Build and scale ad-tech data infrastructure and pipelines ingesting SSP/DSP/GAM sources, transform log-level and CRM data into analytics-ready datasets, maintain warehouses, implement data quality and privacy controls, and support BI and analytics teams for real-time campaign insights.

Design, build, and maintain end-to-end batch and streaming data pipelines on GCP. Migrate legacy warehouses to BigQuery/Bigtable/ClickHouse, orchestrate workflows in Airflow, ensure data quality and observability, and partner with ML engineers on feature pipelines, drift monitoring, and retraining.

Own customer data migration projects end-to-end: review, clean, map, import, validate, and QA records (events, tickets, customers, orders, donations, opt-ins, seating). Troubleshoot and monitor integrations (POS, CRM, marketing, reporting) using SQL, CSVs, APIs, webhooks, logs and admin tools. Build repeatable playbooks, documentation, and processes to reduce engineering dependency and ensure production data accuracy.

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.