Data Engineering Lead

Experience in Data bricks (Hands-on).


Integrating datasets into MCP tools/providers for LLM/agent applications; familiarity with frameworks such as Lang Chain or LlamaIndex


AWS Data Engineering Services (EMR/Glue, Redshift, Aurora, S3, Lambda), Spark, Python, Collibra, Snowflake/Databricks, Tableau.

Roles & Responsibilities
• Ingest and model data from APIs, files/SFTP, and relational sources; implement layered architectures (raw/clean/serving) using PySpark/SQL and dbt, Python.
• Design and operate pipelines with Prefect (or Airflow), including scheduling, retries, parameterization, SLAs, and well documented runbooks.
• Build on cloud data platforms, leveraging S3/ADLS/GCS for storage and a Spark platform (e.g., Databricks or equivalent) for compute; manage jobs, secrets, and access.
• Publish governed data services and manage their lifecycle with Azure API Management (APIM) authentication/authorization, policies, versioning, quotas, and monitoring.
• Enforce data quality and governance through data contracts, validations/tests, lineage, observability, and proactive alerting.
• Optimize performance and cost via partitioning, clustering, query tuning, job sizing, and workload management.
• Uphold security and compliance (e.g., PII handling, encryption, masking) in line with firm standards.
• Collaborate with stakeholders (analytics, AI engineering, and business teams) to translate requirements into reliable, production ready datasets.
• Enable AI/LLM use cases by packaging datasets and metadata for downstream consumption, integrating via Model Context Protocol (MCP) where appropriate.
• Continuously improve platform reliability and developer productivity by automating routine tasks, reducing technical debt, and maintaining clear documentation.
• 4-15 years of professional data engineering experience.
• Strong Python, SQL, and Spark (PySpark) skills, and/or Kafka. • Snowflake (Snow pipe, Tasks, Streams) as a complementary warehouse.
• Databricks (Delta formats, workflows, cataloging) or equivalent Spark platforms.
• Minimum 1 yr of experience in Data bricks (Hands-on).
• Integrating datasets into MCP tools/providers for LLM/agent applications; familiarity with frameworks such as LangChain or LlamaIndex.
• Hands-on experience building ETL/ELT with Prefect (or Airflow), dbt, Spark, and/or Kafka.
• Experience onboarding datasets to cloud data platforms (storage, compute, security, governance).
• Familiarity with Azure/AWS/GCP data services (e.g., S3/ADLS/GCS; Redshift/BigQuery; Glue/ADF).
• Git-based workflows CI/CD and containerization with Docker (Kubernetes a plus).