Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.
This is a hands-on engineering role focused on designing efficient data pipelines, improving data infrastructure, and enabling teams to leverage data effectively.
Responsibilities- Design, build, and maintain scalable data pipelines and ETL/ELT workflows to ingest and transform data from multiple sources
- Develop and optimize batch and near real-time data processing pipelines for analytics and reporting
- Build and maintain data warehouse and data lake structures to support business intelligence and analytics use cases
- Implement and maintain data models that support efficient querying and reporting
- Improve performance and scalability of data systems through query optimization, indexing, and partitioning strategies
- Implement data quality checks, monitoring, and logging to ensure reliability of data pipelines
- Exposure to AI initiatives and experience building data pipelines supporting AI workflows
- Work with data architects and engineering teams to implement scalable data platform designs
- Collaborate with analysts, BI developers, and business stakeholders to deliver data solutions that support business needs
- Maintain documentation for data pipelines, data models, and data workflows
Bachelor's/Master's in Engineering 5-8 years
EXL New York, New York, USA Office
320 Park Avenue, 29th Floor, New York, NY, United States, 10022
EXL Jersey City, New Jersey, USA Office
Jersey City, United States, 0
EXL Newark, New Jersey, USA Office
Newark, United States
Similar Jobs
Pet
Lead Fi’s analytics engineering function by designing dbt models in Databricks, owning the BI platform, and building subscription and collar telemetry data foundations. Develop models supporting acquisition, retention, churn, LTV, and cohort analysis; improve data quality, testing, documentation, and self-service analytics. Partner with Data Engineering on architecture, evaluate new tools, and independently set direction for the company’s transformation and BI layers.
Top Skills:
Business IntelligenceDatabricksDbtETLMedallion ArchitectureSQL
Other
Architect and govern shared analytics infrastructure, data models, semantic layers, business metrics, and self-service BI capabilities. Establish standards for datasets, documentation, testing, and reporting tiers while partnering with Data Engineering, Data Science, Analytics, and business leaders. Own data and BI issues, optimize warehouse performance and costs, and coach practitioners on dimensional modeling and analytics practices.
Top Skills:
AirflowCubeDbtLookerPresetSemantic ViewsSnowflakeSQL
Consulting
Lead development of healthcare analytics products, designing ETL processes, maintaining data pipelines and databases, building visualizations, and applying statistical and machine learning algorithms. Collaborate with product and consulting teams on technology strategy, roadmaps, workflow optimization, and outcome measurement. Mentor junior engineers and contribute to patient-outcome improvements using large-scale EHR data.
Top Skills:
AWSData PipelinesData WarehousingETLGitLinuxMachine LearningMySQLPythonRRelational DatabasesScrumUnix
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
