Top Senior Data Engineer Jobs

Designs, builds, and operates high-throughput streaming and batch data infrastructure for AI products. Responsibilities include developing Kafka, Flink, and Airflow pipelines; managing analytical data layers; optimizing query and pipeline performance; implementing Terraform and GitOps practices; improving observability, reliability, and cost efficiency; partnering with product and research teams; and participating in on-call operations and automation initiatives.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Lead modernization of data platforms, design and maintain ETL/ELT pipelines and data warehouse models, improve data governance and quality, optimize platform performance, and collaborate with IT, business units, and leadership to drive data-driven initiatives and stakeholder alignment.

Build and own the data platform backbone: design ingestion patterns, Snowflake warehouses, governance, and freshness strategy. Implement reliable batch and streaming pipelines, data quality, observability, and onboarding processes. Lead legacy migrations, create data contracts and SLAs, and apply AI-native tooling to automate debugging, documentation, and operational workflows while partnering with Analytics, Engineering, and vendors.

Lead design and ownership of a modern data platform: ingestion, Snowflake governance, pipeline reliability, migrations, data contracts, observability, and AI-enabled tooling. Partner with Analytics, Engineering, and vendors to deliver trustworthy data products and operate production pipelines.

Design, build, and maintain cloud data pipelines and governance on Databricks or Snowflake. Optimize Databricks clusters, implement Delta Lake, automate CI/CD with Azure DevOps, integrate with Azure services, and create monitoring and governance solutions.

Design, implement, and maintain Azure-based ELT/ETL pipelines and data architecture. Migrate and normalize source data into ADLS, enforce quality controls, source control, documentation, and self-service analytics. Optimize storage/processing, audit pipelines, and explore AI-assisted tooling.

Lead design and operation of a cloud-native data platform: set technical direction, build scalable ingestion/processing/warehousing pipelines (batch & streaming), support ML data needs, mentor engineers, drive projects end-to-end, run planning/standups/on-call, and eventually lead a data engineering team.

Own and scale the data platform: design and implement ETL/ELT pipelines, optimize warehouse/lake architecture, support streaming and batch ingestion, enable ML workflows, mentor engineers, write design documents, participate in sprints and on-call rotation, and deliver reliable clinical data at scale to customers.

Design, develop, and maintain enterprise data solutions and pipelines using Azure data platform and Microsoft Fabric. Support data modeling, master data management, medallion architecture, data quality, and integrations to produce trusted, scalable data for analytics and BI.

Design, build, and maintain scalable data pipelines and architectures (ETL, data lake, MDM) using Snowflake and Talend Cloud. Ensure data quality and governance, enable AI-readiness, pilot AI/ML proofs-of-concept, support reporting (Tableau/Power BI), and manage data operations and external vendors.

Build, scale, and maintain data pipelines and transformation layers to ingest diverse sources, ensure data reliability and freshness, optimize data models and compute, create a semantic layer for self-service analytics, support BI reporting, collaborate with cross-functional leadership, and mentor analysts and scientists on engineering best practices.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

Design and implement scalable data ingestion, ETL/ELT pipelines and transformations into Microsoft Fabric using ADF and notebooks (Python/Pandas/Spark). Build and maintain dimensional data warehouse models, ensure data quality, performance tuning, and prepare data for semantic models. Collaborate with stakeholders, participate in code reviews, documentation, and operational handoffs while establishing standards for deployment and governance.

The role involves architecting intelligence infrastructure, building data pipelines, collaborating with other teams, and ensuring data accessibility and security for threat detection systems.

Design, develop, and monitor large-scale cloud-native data solutions and ETL pipelines; support ML model deployment, ensure code hygiene and CI/CD, mentor engineers, produce documentation and runbooks, and engage with clients to deliver analytics-driven solutions.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Lead architecture and implementation of fleet telemetry, time-series storage, streaming pipelines, and CRM/warehouse integrations on AWS. Build tools for proactive fleet health, partner cross-functionally (firmware, manufacturing, ops), support on-call as systems mature, and design LLM-driven automations to increase team leverage.

Design and implement scalable data infrastructure and pipelines on AWS using Spark and Scala. Build fault-tolerant batch and streaming workflows, optimize storage formats and engines, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable data assets and APIs.

Lead design and implementation of Jane's customer data infrastructure: own event collection, pipelines, CDP tooling, schema governance, identity resolution, and attribution. Work cross-functionally with marketing, BI, product, and engineering to set standards, evaluate tooling (e.g., RudderStack), ensure privacy-compliant instrumentation, and build reliable, consistent data delivery to the warehouse.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.