Data Engineer

Fusemachines•New York City, NY

14d•Remote

About The Position

This is a remote full-time consulting position responsible for designing, building, and maintaining the infrastructure required for data integration, storage, processing, and analytics (BI, visualization and Advanced Analytics). We are looking for a skilled Senior Data Engineer with a strong background in Python, SQL, PySpark, Azure, Databricks, Synapse, Azure Data Lake, DevOps and cloud-based large scale data applications with a passion for data quality, performance and cost optimization. The ideal candidate will develop in an Agile environment, contributing to the architecture, design, and implementation of Data products , including migration from Synapse to Azure Data Lake. This role involves hands-on coding, mentoring junior staff and collaboration with multi-disciplined teams to achieve project objectives.

Requirements

Must have a full-time Bachelor's degree in Computer Science or similar
At least 3 years of experience as a data engineer with strong expertise in Databricks, Azure, DevOps, or other hyperscalers.
3+ years of experience with Azure DevOps, GitHub.
Proven experience delivering large scale projects and products for Data and Analytics, as a data engineer, including migrations.
Databricks Certified Associate Developer for Apache Spark
Databricks Certified Data Engineer Associate
Microsoft Certified: Azure Fundamentals
Microsoft Certified: Azure Data Engineer Associate
Strong programming Skills in one or more languages such as Python (must have), Scala, and proficiency in writing efficient and optimized code for data integration, migration, storage, processing and manipulation.
Strong understanding and experience with SQL and writing advanced SQL queries.
Thorough understanding of big data principles, techniques, and best practices.
Strong experience with scalable and distributed Data Processing Technologies such as Spark/ PySpark (must have: experience with Azure Databricks ), DBT and Kafka, to be able to handle large volumes of data.
Solid Databricks development experience with significant Python, PySpark, Spark SQL, Pandas, NumPy in Azure environment.
Strong experience in designing and implementing efficient ELT/ETL processes in Azure and Databricks and using open source solutions being able to develop custom integration solutions as needed.
Skilled in Data Integration from different sources such as APIs, databases, flat files, event streaming.
Expertise in data cleansing, transformation, and validation.
Proficiency with Relational Databases (Oracle, SQL Server, MySQL, Postgres, or similar) and NonSQL Databases (MongoDB or Table).
Good understanding of Data Modeling and Database Design Principles.
Being able to design and implement efficient database schemas that meet the requirements of the data architecture to support data solutions.
Strong experience in designing and implementing Data Warehousing, data lake and data lake house, solutions in Azure and Databricks.
Good experience with Delta Lake, Unity Catalog, Delta Sharing, Delta Live Tables (DLT).
Strong understanding of the software development lifecycle (SDLC), especially Agile methodologies.
Strong knowledge of SDLC tools and technologies Azure DevOps and GitHub, including project management software (Jira, Azure Boards or similar), source code management (GitHub, Azure Repos or similar), CI/CD system (GitHub actions, Azure Pipelines, Jenkins or similar) and binary repository manager (Azure Artifacts or similar).
Strong understanding of DevOps principles, including continuous integration, continuous delivery (CI/CD), infrastructure as code (IaC – Terraform, ARM including hands-on experience), configuration management, automated testing, performance tuning and cost management and optimization.
Strong knowledge in cloud computing specifically in Microsoft Azure services related to data and analytics, such as Azure Data Factory, Azure Databricks , Azure Synapse Analytics , Azure Data Lake , Azure Stream Analytics, SQL Server, Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, etc.
Experience in Orchestration using technologies like Databricks workflows and Apache Airflow.
Strong knowledge of data structures and algorithms and good software engineering practices.
Proven experience migrating from Azure Synapse to Azure Data Lake, or other technologies.
Strong analytical skills to identify and address technical issues, performance bottlenecks, and system failures.
Proficiency in debugging and troubleshooting issues in complex data and analytics environments and pipelines.
Good understanding of Data Quality and Governance, including implementation of data quality checks and monitoring processes to ensure that data is accurate, complete, and consistent.
Strong written and verbal communication skills to collaborate and articulate complex situations concisely with cross-functional teams, including business users, data architects, DevOps engineers, data analysts, data scientists, developers, and operations teams.
Ability to document processes, procedures, and deployment configurations.
Understanding of security practices, including network security groups, Azure Active Directory, encryption, and compliance standards.
Ability to implement security controls and best practices within data and analytics solutions, including proficient knowledge and working experience on various cloud security vulnerabilities and ways to mitigate them.
Self-motivated with the ability to work well in a team, and experienced in mentoring and coaching different members of the team.
A willingness to stay updated with the latest services, Data Engineering trends, and best practices in the field.
Comfortable with picking up new technologies independently and working in a rapidly changing environment with ambiguous requirements.
Care about architecture, observability, testing, and building reliable infrastructure and data pipelines.

Nice To Haves

Microsoft Exam: Designing and Implementing Microsoft DevOps Solutions
Experience with BI solutions including PowerBI is a plus.

Responsibilities

Architect, design, develop, test and maintain high-performance, large-scale, complex data architectures, which support data integration (batch and real-time, ETL and ELT patterns from heterogeneous data systems: APIs and platforms), storage (data lakes, warehouses, data lake houses, etc), processing, orchestration and infrastructure.
Ensuring the scalability, reliability, and performance of data systems, focusing on Databricks and Azure.
Contribute to detailed design, architectural discussions, and customer requirements sessions.
Actively participate in the design, development, and testing of big data products.
Construct and fine-tune Apache Spark jobs and clusters within the Databricks platform.
Migrate out of Azure Synapse to Azure Data Lake or other technologies.
Assess best practices and design schemas that match business needs for delivering a modern analytics solution (descriptive, diagnostic, predictive, prescriptive).
Design and implement data models and schemas that support efficient data processing and analytics.
Design and develop clear, maintainable code with automated testing using Pytest, unittest, integration tests, performance tests, regression tests, etc.
Collaborating with cross-functional teams and Product, Engineering, Data Scientists and Analysts to understand data requirements and develop data solutions, including reusable components meeting product deliverables.
Evaluating and implementing new technologies and tools to improve data integration, data processing, storage and analysis.
Evaluate, design, implement and maintain data governance solutions: cataloging, lineage, data quality and data governance frameworks that are suitable for a modern analytics solution, considering industry-standard best practices and patterns.
Continuously monitor and fine-tune workloads and clusters to achieve optimal performance.
Provide guidance and mentorship to junior team members, sharing knowledge and best practices.
Maintain clear and comprehensive documentation of the solutions, configurations, and best practices implemented.
Promote and enforce best practices in data engineering, data governance, and data quality.
Ensure data quality and accuracy.
Design, Implement and maintain data security and privacy measures.
Be an active member of an Agile team, participating in all ceremonies and continuous improvement activities, being able to work independently as well as collaboratively.