Top Data & Analytics Jobs

Design and build ETL/ELT pipelines and analytics workflows, develop advanced Tableau dashboards for operational and executive reporting, translate complex data into clear visual stories, partner with engineering and analytics teams to productionize dashboards, and support platform performance and data integrity.

Lead SAP S/4HANA data migration activities: extract, map, transform, load, validate, and reconcile master and transactional data using BODS, Migration Cockpit, LSMW/BDC and HANA SQL. Support mock loads, cutover execution, defect remediation, and collaborate with functional owners to cleanse and correct source data.

Lead design, build, and maintenance of scalable data pipelines, sandbox databases, and analytics dashboards. Investigate and resolve complex data discrepancies, perform ETL/ELT, data modeling, and performance tuning using Databricks, Starburst, Kafka, SQL, and Python. Partner cross-functionally, enforce data governance, document processes, and mentor team members to support research prototyping and business insights.

Design, build, and maintain end-to-end production data pipelines and ontologies in Palantir Foundry. Ensure governance, data quality, lineage, monitoring, and reliability. Translate business requirements into governed data products, collaborate with analytics and business teams, apply software engineering best practices, and provide technical guidance across the data organization.

Build, test, and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Work across ingestion, transformation, modelling, optimisation and data quality, implement reconciliation and monitoring controls, partner with stakeholders, and contribute reusable tooling and platform improvements to enable analytics and AI use cases.

Design, build, test and support batch and streaming data pipelines and curated datasets on a Lakehouse and AI data platform. Implement data modelling, partitioning, schema evolution, data quality, reconciliation, testing and monitoring. Partner with platform teams and data consumers, contribute reusable tooling and standards, and lead delivery and mentorship for junior engineers.

Design, build, and maintain Databricks-based ETL/ELT pipelines, data lakes, and warehouses for genomic, phenotypic, and operational data. Optimize performance and cost, enforce data quality, integrate lab and sequencing sources, support reporting and analytics, and collaborate with geneticists and bioinformaticians to deliver governed data products for scientific and business use.

Build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Work across ingestion, transformation, modelling, optimisation and data quality, contribute reusable tooling, ensure production reliability, and partner with consumers and platform teams to deliver scalable, well-governed data products for analytics and AI use cases.

Senior Data Modeler responsible for designing logical and physical data models, implementing database designs, planning data collection/cleansing, coordinating documentation, and partnering with data transformation teams to enable ETL/ELT pipelines, hybrid on-prem/cloud data movement, and reporting solutions.

Design, build, and govern a governed Azure-centric data warehouse unifying ticketing, loyalty, and F&B data. Own identity resolution, pipeline development (Python, SQL, dbt, Airflow), environment governance, architectural audits, and mentor engineers while partnering with business stakeholders to deliver trusted data products.

Design and build cloud-based data applications and pipelines to analyze clinical data, enrich and provision datasets, and support clinical/operational processes. Mentor junior engineers, drive R&D for repeatable templates, collaborate with Product/Platform/Architecture, and prioritize source control, documentation, and simple solutions using modern data, ETL, and LLM tooling.

Design and build end-to-end data pipelines, integrations, and APIs; maintain the data warehouse and ensure data accuracy, reliability, and security. Partner with cross-functional teams to translate needs into scalable solutions, mentor engineers, provide operational support, document integrations, and contribute to governance and enterprise architecture.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows ingesting upstream production and operational data. Architect data integrations with SCADA/ERP sources, ensure data quality, performance monitoring, capacity planning, disaster recovery, documentation, and mentor junior engineers while aligning data engineering priorities with business objectives.

Design and build ETL/ELT pipelines, canonical client/account data models, and revenue attribution logic for equities and adjacent products. Resolve entity and reconciliation issues across CRM, OMS/EMS, and finance systems, partnering with Front Office, Finance, Compliance, and Operations to ensure governance and accurate management reporting.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

Build and maintain scalable pipelines to ingest, clean, and integrate large multimodal datasets. Own ingestion across APIs, files, cloud storage, and warehouses. Design linkage, entity resolution, deduplication, and schema harmonization. Implement data quality checks, validation, and documentation. Collaborate with engineering, research, and deployment teams to prepare integrated data for simulations and evaluate new data sources.

Senior technical leader embedded in a Scrum team to design, build, and maintain scalable batch and real-time data pipelines and services; lead data modeling and warehousing; enforce data engineering best practices, quality frameworks, SLAs/SLOs, and observability; translate product and analytics needs into durable data platforms; mentor engineers and influence cross-team technical direction.

Senior Data Engineer designs, builds, and maintains scalable cloud data pipelines and platforms (Snowflake/Databricks/Microsoft Fabric), implements ELT/ETL, optimizes data models, supports BI/AI use cases, participates in solution design and client engagements, mentors junior engineers, and ensures data products are documented and production-ready.

Design, build, and optimize the company lakehouse environment and ETL/ELT pipelines (Databricks/Azure/Fabric). Develop semantic models, governed metrics, data quality controls, monitoring, and CI/CD practices. Partner with business, security, and analytics teams to steward master data, implement governance and access controls, and enable reliable datasets for reporting and AI use cases.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Identify, evaluate, and integrate external data sources to enhance the platform. Build and maintain automated ETL pipelines, ensure data quality and governance, document workflows, perform cost-benefit analyses of vendors/tools, and present recommendations to leadership while collaborating with product and engineering teams.

Design, build, and maintain reliable data pipelines and data warehouse artifacts. Collaborate with engineering, product, and research teams to prototype solutions, document data products, and ensure quality and scalability.

Lead design and delivery of Snowflake-based data solutions, data models, ETL pipelines and data warehouse applications. Provide architecture direction, drive technology transformation and POCs, evangelize modern data architecture, collaborate with global stakeholders and Agile teams, and apply advanced analytics (ML/AI/GenAI) while ensuring data security and PII management.

Design, build, and maintain reliable, efficient data pipelines and infrastructure; guide data architecture and proofs-of-concept; collaborate with engineering, product, and research to develop data products; document data warehouse and data products; write, QA, and maintain data engineering code and provide technical leadership on projects.