Job Title :- Data Engineer
As a Data Engineer II, you will design, build, and optimize modern data platforms that power advanced analytics and AI solutions.
You’ll collaborate with clients and interdisciplinary teams to architect scalable pipelines, manage secure and compliant data environments, and unlock the value of complex datasets across industries. You’ll sharpen your expertise by working on innovative projects, contributing to R&D, and learning from top-tier talent in a dynamic, global environment.
Your work will drive lasting impact. By ensuring data is accurate, accessible, and production-ready, you’ll enable clients to accelerate digital transformations, adopt AI responsibly, and achieve measurable business outcomes. Here’s how you might contribute in a given year:
- Develop a streaming data platform to integrate telemetry for predictive maintenance in aerospace systems
- Implement secure data pipelines that reduce time-to-insight for a Fortune 500 utility company
- Optimize large-scale batch and streaming workflows for a global financial services client, cutting infrastructure costs while improving performance.
- Develop pipelines for embeddings and vector databases to enable retrieval-augmented generation (RAG) for a global defense client.
You’ll work in cross-functional Agile teams with Data Scientists, Machine Learning Engineers, Designers, and domain experts to deliver high-quality analytics solutions. Partnering closely with clients—from data owners to C-level executives—you’ll shape data ecosystems that drive innovation and long-term resilience.
This role offers an exceptional environment to grow as a technologist and collaborator. You’ll develop expertise at the intersection of technology and business by tackling diverse challenges while collaborating with some of the best technical and business talent in the world.
There is flexibility to hire at the Engineer I/II or Senior Engineer level, depending on your experience
<.> YOUR QUALIFICATIONS AND SKILLS
- Degree in Computer Science, Business Analytics, Engineering, Mathematics, or related field
- 4+ years of professional experience in data engineering, software engineering, or adjacent technical roles
- Proficiency in Python, Scala, or Java for production-grade pipelines, with strong skills in SQL and PySpark
- Hands-on experience with cloud platforms such as (AWS, Google Cloud Platform, Azure, Oracle) and modern data storage/warehouse solutions such as Snowflake, BigQuery, Redshift, and Delta Lake
- Practical experience with Databricks, AWS Glue, and transformation frameworks like dbt, Dataform, or Databricks Asset Bundles
- Knowledge of distributed systems such as (Spark, Dask, Flink) and streaming platforms (Kafka, Kinesis, Pulsar) for real-time and batch processing
- Familiarity with workflow orchestration tools such as (Airflow, Dagster, Prefect), CI/CD for data workflows, and infrastructure-as-code (Terraform, CloudFormation)
- Understanding of DataOps principles including pipeline monitoring, testing, and automation, with exposure to observability tools such as Datadog, Prometheus, and Great Expectations
- Exposure to ML platforms such as (Databricks, SageMaker, Vertex AI), MLOps best practices, and GenAI toolkits (LangChain, LlamaIndex, Hugging Face)
- Familiarity with vector databases and understanding of low latency serving patterns is a plus
- Strong communication, time management, and resilience, with the ability to align technical solutions to business value
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