Top Data & Analytics Jobs

Lead design, build, and optimization of the enterprise data warehouse and production data pipelines. Own platform architecture, integrate legacy systems (AS400), support Power BI semantic models (DAX), implement monitoring, CI/CD and governance, and mentor engineers to transition from consultant-run platforms to an internally managed, scalable data ecosystem.

Design, own, and scale an enterprise Databricks/Azure data platform supporting analytics and interoperability. Lead integration architecture (APIs, ADF, event-driven), build high-performance Python/SQL pipelines, enforce governance (Unity Catalog), ensure healthcare compliance, mentor engineers, and drive CI/CD and IaC for reliable, secure PHI/PII handling.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software, participate across the SDLC, perform database design and performance tuning, troubleshoot production issues, and collaborate with internal clients. Work primarily with Hadoop, Spark, Impala, Hive in Unix/Linux and Windows environments within financial/consumer banking contexts.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software across the full SDLC, perform problem investigation and performance tuning, work in Unix/Windows environments, and collaborate with finance domain teams. Use Hadoop, Spark, Impala, Hive, batch and online processing, shell/Perl scripting, and ETL/job-scheduling tools as needed.

Lead design and implementation of scalable real-time and batch data pipelines for programmatic advertising. Ingest, normalize, transform, and model bid stream, impression, and revenue data; maintain data warehouses and lakes; implement data quality and privacy compliance; support BI teams and optimize yield through analytics and feature engineering.

Provide technical leadership for the MFS data platform, collaborate with cross-functional engineering teams to improve platform adoption, mentor engineers, and design and develop large-scale distributed systems using Big Data technologies and Kafka.

Design, build, and optimize enterprise-scale data pipelines and data models in Databricks to support analytics, AI, and operational systems. Lead MDM initiatives, implement data governance, lineage, and quality frameworks, and collaborate with engineering and business teams to ensure scalable, reliable, and cost-efficient data platforms.

Design, build, and deploy an ETL-based transactional data system to read and manage information state and transmit data via web services. Implement solution from provided design, perform unit testing, deploy to development/test, and collaborate with QA and DPS to resolve defects.

Lead design, development and delivery of enterprise data and analytics solutions using Azure Data Factory, Databricks and Power BI. Manage data warehouse, ETL, stored procedures and data quality; provide technical leadership, stakeholder engagement and agile backlog management to support research and student services.

The Data Operations Engineer will monitor and optimize trading processes, enhance integration and deployment pipelines, ensure quality control, and lead continuous improvement initiatives while documenting and reporting on production performance.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

The Senior Data and Solutions Engineer develops data pipelines, manages analytics frameworks, and collaborates with stakeholders to improve data quality and reporting consistency.

The Senior Data Privacy Engineer will design, develop, and support data discovery, privacy compliance, and data protection solutions, focusing on OneTrust and Informatica tools while collaborating across teams and mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

Lead the technical direction for data platforms, develop high-throughput data processing systems, mentor engineers, and ensure production reliability.

Lead design and delivery of enterprise data platforms using Snowflake and AWS. Architect ELT/CDC pipelines, implement Data Vault 2.0 and dimensional models, optimize Snowflake performance, mentor engineers, partner with stakeholders, and ensure operational reliability and observability for analytics and BI workloads.

Design, ingest, validate, and manage complex financial and annuity data from external partners. Build and maintain ETL pipelines, SQL queries, and data quality controls; investigate data anomalies; collaborate with Accounting, Actuarial, and Technology; support reporting and process automation to improve financial and regulatory workflows.

Lead design, development, and optimization of ETL/ELT pipelines and data products for telematics and third-party data. Ingest large-scale datasets into the enterprise data lake/lakehouse, implement CI/CD and DataOps practices, orchestration (MWAA/Autosys/Control-M), observability, and resilient self-healing pipelines. Mentor team members, collaborate with architects, data scientists, and BI teams, run POCs, and apply RAG and AI-driven systems while ensuring governance, security, performance, and data quality.

Design, build, and maintain scalable semantic 360 data models and transformation pipelines. Implement cleansing, enrichment, business rules, KPIs, and scoring algorithms using SQL and Python/Scala. Employ AI coding assistants and modern AI engineering practices to accelerate development while ensuring traceability, data quality, performance, and alignment with product and analytics needs.

Lead Data Engineer/ETL role requiring 5-10 years of Informatica ETL experience. Responsibilities focus on BDM, TDM, data quality, big data, data warehousing, and star schema design. Strong verbal and written communication and a technical bachelor’s degree required. Unix experience is a plus.

Design, build, and optimize SQL Server-based data layers and Azure data solutions. Own query performance, indexing, concurrency, CDC pipelines, ORM data access patterns (C#), and database CI/CD. Partner with engineers and architects, mentor team members, and ensure scalable, low-latency data platforms.

Hands-on data and integration engineer responsible for building, supporting, and automating Canoe-related workflows and integrations. Develop and maintain data pipelines, SQL queries, validations, reconciliations, and monitoring to deliver alternative investment data to Snowflake, Addepar, reporting tools, and downstream systems. Troubleshoot data quality issues, support production feeds, manage exceptions, document mappings and runbooks, and collaborate with investment, reporting, technology, and vendor teams. Hybrid role requiring Baltimore office presence three days per week.

Lead a team building and operating large-scale, reliable data pipelines and warehouses. Mentor engineers, partner cross-functionally, manage SLAs, embed AI/LLM-driven Data Ops, and drive end-to-end delivery of canonical datasets and data infrastructure.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

Build and document DBT models in Snowflake, develop and maintain Tableau dashboards, handle data operations and troubleshooting, modernize data infrastructure, and own stakeholder relationships and requirements.