Description We are seeking a strong Data Engineer with advanced expertise in Databricks and PySpark. The successful candidate will be a key contributor to critical projects, including migrating Palantir data transformation pipelines to Databricks Notebooks, designing and implementing incremental data pipelines, and orchestrating workflows in Azure Databricks. Key Responsibilities Migrate Palantir data pipelines to Databricks Notebooks, leveraging PySpark for complex transformations. Replace proprietary Palantir libraries with open source or custom Pyspark implementations Design, build, and maintain incremental data load pipelines to handle dynamic updates from various sources, ensuring scalability and efficiency. Develop robust data ingestion pipelines to load data into the Databricks Bronze layer from relational databases, APIs, and file systems. Implement incremental data transformation workflows to update silver and gold layer datasets in near real-time, adhering to Delta Lake best practices. Integrate Airflow with Databricks to orchestrate end-to-end workflows, including dependency management, error handling, and scheduling. Understand business and technical requirements, translating them into scalable Databricks solutions. Optimize Spark jobs and queries for performance, scalability, and cost-efficiency in a distributed environment. Implement robust data quality checks, monitoring solutions, and governance frameworks within Databricks. Collaborate with team members on Databricks best practices, reusable solutions, and incremental loading strategies. Required Qualifications Bachelors degree in computer science, Information Systems, or a related discipline. 6+ years of hands-on experience with Databricks, including expertise in PySpark. Proven experience in incremental data loading techniques into Databricks, leveraging Delta Lake's features (e.g., time travel, MERGE INTO). Strong understanding of data warehousing concepts, including data partitioning, and indexing for efficient querying. Solid knowledge of Azure Cloud Services, particularly Azure Databricks and Azure Data Lake Storage. Familiarity with version control systems (e.g., Git) and CI/CD pipelines for data engineering workflows. Excellent analytical and problem-solving skills with a focus on detail-oriented development. Preferred Qualifications Proficiency in Palantir and experience in migrating Palantir data pipelines to Databricks. Expertise in Airflow integration for workflow orchestration, including designing and managing DAGs. Familiarity with advanced Airflow features, such as SLA monitoring and external task dependencies. Advanced knowledge of Delta Lake optimizations, such as compaction, Z-ordering, and vacuuming. Experience with real-time streaming data pipelines using tools like Kafka or Azure Event Hubs. Experience with building, updating, deploying, finetuning ML models Certifications such as Databricks Certified Associate Developer for Apache Spark or equivalent. Experience in Agile development methodologies. (ref:hirist.tech)
Description Job Title : Lead Data Consultant / Senior Data Engineer Experience Level : 10+ Years (with deep Azure experience) Employment Type : Full-time / Contract Role Overview As a Lead Data Consultant / Senior Data Engineer, you will design, develop, and lead the delivery of enterprise-scale, cloud-native data platforms for our clients, with a particular focus on Azure, Databricks, and modern Lakehouse architectures. Working under the direction of the Principal Data Consultant you will help shape go-to-market technical assets and production-ready solutions across data engineering, system integration, governance, and AI/ML-enablement. This is a hands-on consulting role requiring both strong implementation expertise and the ability to influence design, compliance, and delivery decisions across diverse enterprise environmentsparticularly in Financial Services and Insurance (FSI). Key Responsibilities Architect and build Lakehouse solutions with bronze/silver/gold layers using Delta Lake and Databricks Implement scalable ETL/ELT pipelines using Azure Data Factory, Airflow, Databricks, and PySpark Define enterprise-level data architectures, including lineage, governance, and integration patterns Enable agent orchestration and validation pipelines to support dynamic, AIenabled processing flows Lead development of data ingestion, transformation, and orchestration frameworks across cloud-native environments Guide implementation of data governance and compliance standards specific to FSI, including lineage tracking and access controls Collaborate with cross-functional teams (platform, ML, API, compliance) to design integrated data + AI systems Develop CI/CD pipelines and IaC (Terraform) modules to automate provisioning and deployment of data infrastructure Mentor other engineers and ensure reusable, modular, well-documented assets are delivered Technical Skills & Experience Data Platforms & Engineering : 10+ years of experience in data engineering, with deep Azure cloud experience Proven experience designing Lakehouse architectures and implementing bronze/silver/gold/curated layers Hands-on expertise with Databricks, Delta Lake, PySpark, and SQL Experience integrating Kafka, RDBMS, and unstructured data into cloud pipelines Knowledge of DLT-Meta frameworks, metadata-driven ELT development, and data mesh concepts Familiarity with GenAI, and its integration with structured/unstructured data System Architecture Ability to design systems spanning data layers, API integrations, and AI orchestration components Knowledge of agentic systems, validation workflows, and event-driven orchestration Governance & Compliance Strong understanding of data lineage, data quality, and access control models Experience applying FSI regulatory standards, including auditing and privacy best practices Cloud & DevOps Proficiency in Azure services, especially ADF, Synapse, Blob Storage, Key Vault, and Managed Identity CI/CD using GitHub, GitLab, or Azure DevOps, with automated deployment patterns Infrastructure automation using Terraform with Git-based workflows Development environments : VSCode, Python scripting, Git version control Non-Technical & Consulting Skills Agile delivery experience : refinement, estimation, MVP definition, backlog grooming Ability to translate technical solutions into high-level architecture artifacts and documentation (Markdown, Confluence, Lucidchart) Comfortable reviewing existing codebases and recommending paths for reuse or modernization Proven ability to mentor and unblock delivery teams, with experience guiding junior and mid-level engineers Strong communication skills for client interaction, executive presentations, and cross-team coordination (ref:hirist.tech)