Lead Data Engineer

Strong expertise in Python, SQL, PySpark and AWS in an Agile environment, with a proven track record of building and optimizing data pipelines, architectures, and datasets, and proven experience in data storage, modeling, management, lake, warehousing, processing/transformation, integration, cleansing, validation and analytics.

Senior person who can understand requirements and design end to end solutions with minimal oversight.

Strong programming Skills in one or more languages such as Python , Scala, and proficient in writing efficient and optimized code for data integration, storage, processing and manipulation.

Strong knowledge SDLC tools and technologies,  including project management software (Jira or similar), source code management (GitHub or similar), CI/CD system (GitHub actions, AWS CodeBuild or similar) and binary repository manager (AWS CodeArtifact or similar).

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 SQL skills and experience working with complex data sets, Enterprise Data Warehouse and writing advanced SQL queries.

Proficient with Relational Databases (RDS, MySQL, Postgres, or similar) and NonSQL Databases (Cassandra, MongoDB, Neo4j, etc.).

Skilled in Data Integration from different sources such as APIs, databases, flat files, event streaming.  Strong experience in implementing data pipelines and efficient ELT/ETL processes, batch and real-time, in AWS and using open source solutions, being able to develop custom integration solutions as needed, including Data Integration from different sources such as APIs (PoS integrations is a plus), ERP (Oracle and Allegra are a plus), databases, flat files, Apache Parquet, event streaming, including cleansing, transformation and validation of the data.

Strong experience with scalable and distributed Data Technologies such as Spark/ PySpark , DBT and Kafka , to be able to handle large volumes of data.

Experience with stream-processing systems: Storm, Spark-Streaming, etc.

is a plus.

Strong experience in designing and implementing Data Warehousing solutions in AWS with Redshift .

Demonstrated experience in designing and implementing efficient ELT/ETL processes that extract data from source systems, transform it (DBT), and load it into the data warehouse.

Strong experience in Orchestration using Apache Airflow.

Expert in Cloud Computing in AWS, including deep knowledge of a variety of AWS services like Lambda, Kinesis, S3 , Lake Formation, EC2, EMR , ECS/ECR, IAM, CloudWatch, etc 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.

Good understanding of BI solutions including Looker and LookML (Looker Modeling Language).

Strong knowledge and hands-on experience of DevOps principles, tools and technologies (GitHub and AWS DevOps) including continuous integration, continuous delivery (CI/CD), infrastructure as code (IaC – Terraform), configuration management, automated testing, performance tuning and cost management and optimization.

Good Problem-Solving skills: being able to troubleshoot data processing pipelines and identify performance bottlenecks and other issues.

Possesses strong leadership skills with a willingness to lead, create Ideas, and be assertive.

Strong project management and organizational skills.

Excellent communication skills to collaborate with cross-functional teams, including business users, data architects, DevOps/DataOps/MLOps engineers, data analyst, data scientists, developers, and operations teams.

Essential to convey complex technical concepts and insights to non-technical stakeholders effectively.

Ability to document processes, procedures, and deployment configurations.

Responsibilities:  Design, implement, deploy, test and maintain highly scalable and efficient data architectures, defining and maintaining standards and best practices for data management independently with minimal guidance.

Ensuring the scalability, reliability, quality and performance of data systems.

Mentoring and guiding junior/mid-level data engineers.

Collaborating with Product, Engineering, Data Scientists and Analysts to understand data requirements and develop data solutions, including reusable components.

Evaluating and implementing new technologies and tools to improve data integration, data processing and analysis.

Design architecture, observability and testing strategies, and building reliable infrastructure and data pipelines.

Takes ownership of storage layer, data management tasks, including schema design, indexing, and performance tuning.

Swiftly address and resolve complex data engineering issues, incidents and resolve bottlenecks in SQL queries and database operations.

Conduct Discovery on existing Data Infrastructure and Proposed Architecture.

Evaluate and implement cutting-edge technologies and methodologies and continue learning and expanding skills in data engineering and cloud platforms,  to improve and modernize existing data systems.

Evaluate, design, and implement data governance solutions: cataloging, lineage, quality and data governance frameworks that are suitable for a modern analytics solution, considering industry-standard best practices and patterns.  Define and document data engineering architectures, processes and data flows.

Assess best practices and design schemas that match business needs for delivering a modern analytics solution (descriptive, diagnostic, predictive, prescriptive).

Be an active member of our Agile team, participating in all ceremonies and continuous improvement activities.

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

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