Top Remote Data Engineer Jobs in Indianapolis, IN

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Senior Data Engineer on PwC's Managed Data, Analytics & Insights team to design, build and manage advanced data ecosystems. Responsibilities include designing data solutions and scalable pipelines, solving complex problems, mentoring junior staff, maintaining high delivery standards, and building client relationships while aligning solutions to business context.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Build and own the companys modern data platform: ingestion, orchestration, Snowflake architecture, dbt transformations and CI/CD, observability, governance, and a semantic layer to enable trusted analytics and production AI/ML workloads. Partner cross-functionally and automate recurring operational pain points into reusable platform capabilities.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Lead architecture and implementation of scalable batch and streaming ETL/ELT pipelines, data services, and graph-serving models for Observability & Insights. Own technical strategy, drive performance and cost improvements, enforce data governance and observability, lead cross-team initiatives, participate in on-call and incident follow-through, and partner with product and engineering to deliver low-latency graph exploration at extreme scale.

The Data Engineer will design, build, and deploy scalable data solutions, optimize data flows, and implement ETL processes, leveraging various cloud platforms and technologies.

The Principal MarTech Data Engineer will design data architecture, build data pipelines, ensure data quality, and lead cross-functional technical initiatives to enhance marketing technology and data privacy infrastructure.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.

Design, build, and maintain ETL/ELT pipelines and data architectures to ingest, normalize, and integrate operational and infrastructure data. Develop API integrations, database queries, and telemetry pipelines; create dashboards and reports; troubleshoot production data quality and collection issues; produce documentation and collaborate with stakeholders to support compliance tracking and operational analytics.

Design, build, and maintain scalable ETL/ELT data pipelines and data models integrating USMC logistics systems (GCSSMC, TLS). Ensure data quality, RMF/cybersecurity compliance, support analytics, dashboards, APIs, and collaborate with NIWC Atlantic, stakeholders, and cross-functional teams to improve logistics visibility and readiness.

Build and maintain scalable batch and streaming ETL/ELT pipelines, data models, and data warehouse architectures. Implement orchestration, quality, governance, and monitoring. Create self-service dashboards, enable ML/AI-ready datasets and feature pipelines, optimize performance, and collaborate with cross-functional teams to deliver analytics and AI-enabled solutions for customer experience and post-sales operations.

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.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.