Top Data & Analytics Jobs

Builds and supports production ETL/ELT pipelines for a Microsoft Fabric Data Lakehouse using Data Factory, Spark, T-SQL, Python, and PySpark. Implements medallion architecture, dimensional models, data quality controls, source integrations, governance, security, and Power BI semantic models. The role also optimizes pipeline performance, manages CI/CD deployments with Git and Azure DevOps, documents lineage and runbooks, supports production issues, and partners with business stakeholders to deliver analytics- and AI-ready datasets.

Build, test, and support Databricks data pipelines using Python, PySpark, and SQL. Ingest, clean, validate, and transform source data into reusable layered tables for analytics, reporting, and automation. Document pipelines and business rules, resolve data quality issues, support production delivery, and collaborate with stakeholders and senior engineers. The role also involves adapting to new tools and business areas while supporting a federal government client.

The Associate Data Engineer role is listed with KKR, but the provided description contains only company information and equal-opportunity notices. No specific responsibilities, technologies, qualifications, salary, travel, or clearance requirements are provided.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Designs and leads scalable data pipelines, data products, and cloud data solutions using AWS, Snowflake, dbt, SQL, and Python. Develops batch and near-real-time ingestion, curated data models, and AI-ready platforms while ensuring data quality, governance, observability, reliability, and performance. Establishes engineering standards, CI/CD practices, and infrastructure-as-code, troubleshoots production issues, collaborates with analytics and AI/ML teams, and mentors engineers.

Designs, builds, supports, and optimizes enterprise-scale Azure data platforms and pipelines. Responsibilities include ETL/ELT development, data validation and reconciliation, performance and cost optimization, governance and security compliance, incident resolution, SLA management, and collaboration with technical and business stakeholders. The role participates in Agile delivery and an on-call roster while delivering strategic data initiatives for Rabobank.

Curate, review, organize, and upload biologically oriented scientific and intelligence datasets into a web-based application. Responsibilities include metadata compilation, data integrity validation, structured repository management, GitLab task tracking and documentation, and collaboration with clients, data curators, and developers. The role requires an active TS/SCI clearance with CI Poly, U.S. citizenship, and relevant data management experience.

Leads the design, orchestration, and optimization of complex real-time data pipelines, ETL processes, data architectures, warehouses, and visualization solutions. Implements data governance, privacy, and security controls; optimizes cloud costs; integrates systems and vendor tools; resolves escalations; and communicates data strategies to stakeholders. Mentors junior team members and aligns scalable data solutions with organizational goals.

Designs, builds, optimizes, and supports scalable AWS-based data infrastructure, pipelines, data models, and ETL/ELT processes using Python, SQL, PySpark, Databricks, and Spark. The role supports analytics, reporting, and business intelligence through production monitoring, troubleshooting, testing, CI/CD, documentation, and cross-functional collaboration with BI, Product, Engineering, Data, and client-facing teams.

Develop and maintain Azure-based data platforms, architectures, pipelines, and processing solutions. Build scalable integrations with Azure Data Factory and Databricks, transform data for business intelligence, implement infrastructure as code and CI/CD, and perform testing, governance, documentation, and data quality activities. Collaborate with BI developers, business stakeholders, and source-system owners to translate requirements into secure, maintainable datasets and support future data warehousing, machine learning, streaming, and visualization needs.

Build and maintain Databricks data pipelines using Python, PySpark, Spark SQL, and Delta Lake. Ingest data from databases, APIs, files, and cloud storage; implement medallion architecture; optimize Spark workloads; add data quality checks; manage schema changes; and monitor workflows. The role also involves Git-based development, documentation, code reviews, CI/CD participation, and client-facing communication. It is hybrid and based at client sites in McLean, Virginia.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

Designs dashboards, reports, interactive analytics applications, and web-based data experiences. Translates business questions into intuitive visualizations, identifies trends and anomalies, establishes dashboard standards, and partners with stakeholders and application developers. The role also integrates AI-enabled capabilities, improves existing analytics solutions, and promotes usability, adoption, and data-driven decision-making across the organization.

Develop and maintain data lake and data mart architecture, translate client requirements into migration plans, extract and migrate data while preserving integrity and security, create data visualizations, and collaborate with QA to resolve defects. The role requires client-focused travel and/or relocation to unanticipated U.S. locations as project needs dictate.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Supports Microsoft SQL Server databases, T-SQL queries, integrations, scheduled jobs, and data integrity. Provides secondary support for Aderant Expert financial applications, including configuration, troubleshooting, testing, upgrades, and documentation. Assists with release preparation, deployment validation, application administration, and issue resolution while collaborating with IT, accounting, vendors, and project teams under senior guidance.

Build and operate production data pipelines connecting marketing, CRM, licensing, payment, and operational systems with Databricks. Develop PySpark and SQL ETL/ELT workflows, dimensional models, APIs, data quality monitoring, identity resolution, and customer segmentation data flows. Support HubSpot enrichment, lifecycle marketing, campaign attribution, funnel reporting, and marketing ROI analytics while maintaining documentation, alerting, and incident response processes.

Develop data analytics and visual analytic applications using structured and semi-structured data. Design enterprise databases, build large relational databases, integrate systems, optimize data pipelines, and improve system performance. The role requires Java, AWS, ETL, Spark/Scala, SQL/PLSQL, PostgreSQL, NiFi, Informatica, Linux, an active Top Secret/SCI-eligible clearance, and potential CI polygraph eligibility.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Designs and deploys scalable data pipelines and ETL/ELT processes using Azure and Databricks. Develops Python, PySpark, and SQL solutions, builds relational and analytical data models, optimizes pipeline performance, and ensures data quality. Serves as a technical SME for vendor developers through code reviews, quality standards, delivery oversight, and collaboration with product owners and business stakeholders.

Designs, develops, and maintains data models, integration pipelines, SQL solutions, databases, reporting systems, and analytical applications for DSNY. Performs data profiling, source-to-target mapping, data quality assessment, ETL development, documentation, and enterprise data architecture work. Collaborates with business, technical, and external stakeholders to support operational reporting, analytics, data governance, interoperability, and reliable IT systems across multiple platforms.

Build and maintain data infrastructure, ingestion pipelines, data marts, governed BI models, and Looker dashboards. Ensure data quality, platform performance, lineage, access controls, and consistency across business entities. Collaborate with technical and operational teams, support AI solution integration, train users, document best practices, and lead complex analytics projects using Agile methods.

Leads data engineering within an agile team, designing secure and scalable data platforms, pipelines, architectures, storage, access, analytics, governance, backup, and recovery strategies. Develops advanced data models, evaluates security controls, documents best practices, and drives responsible AI-assisted engineering adoption. Provides technical leadership through architecture decisions, mentoring, cross-team influence, and operational support using Python, Databricks, Spark, AWS, and modern lakehouse patterns.

Designs and maintains enterprise data architectures and ETL pipelines supporting mission-critical GEOINT systems. Assesses geospatial technologies, workflows, and processes; contributes to GIS strategy and technology roadmaps; develops architecture views; documents production processes and performance metrics; identifies infrastructure and performance improvements; and supports lifecycle, configuration, and change management for federal mission systems.

Build and support Python data applications, migrate systems to Azure, develop ETL pipelines, manage data lake resources, and design scalable hybrid cloud architectures. The role also involves implementing statistical and machine learning models, improving data science infrastructure, documenting solutions, and supporting analytics databases.