Data Engineer

Job Title: Sr Data Engineer Type: Contract Location: Miami FL | Dallas, Texas Company Overview The Senior Data Engineer will play a pivotal role in building and maintaining scalable, reliable, and high-performance data pipelines and enterprise analytics infrastructure. This role is ideal for someone who thrives in a collaborative environment, enjoys mentoring others, and wants to drive innovation in data engineering across the organization. Your Responsibilities on the Team Lead the design and implementation of scalable, high-performance data architectures that support diverse data sources, including structured, semi-structured, and unstructured data. Architect and build data pipelines that can process and analyze large-scale datasets in real-time and batch modes. Mentor and guide junior data engineers in best practices, code quality, and technical skills, fostering a culture of continuous learning. Ensure the reliability, efficiency, and security of data pipelines by implementing monitoring, alerting, and automated recovery mechanisms. Collaborate with cross-functional teams, including data scientists, analysts, and product managers, to align data solutions with business needs and goals. Lead the adoption of new technologies and tools that enhance the data engineering capabilities of the team. Oversee the development of data models, schemas, and data marts that enable efficient data analysis and reporting. Implement data governance frameworks, including data lineage, metadata management, and data quality standards. Your Toolbox Bachelor’s degree in Computer Science, Information Technology, Management Information Systems, or a related field. 6+ years of experience in data engineering or a related role, with demonstrated success in delivering enterprise‑scale data solutions. Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning. Experience with cloud data warehouses like Snowflake, Databricks, and understanding of their appropriate use cases. Strong programming skills in Python or Java, with experience in data processing frameworks (e.g., Apache Spark, Hadoop). Experience with cloud platforms (AWS, Azure, GCP) and administration, such as AWS Redshift, Azure Synapse, or Google BigQuery. Proficiency with big data technologies, including Hadoop, Spark, Kafka, and HBase, with experience in distributed data processing. Expertise with data orchestration tools, such as MWAA/Airflow, for scheduling and managing data workflows. Experience with reporting tools such as Power BI and Tableau is a plus. Understanding of data security practices, including encryption, access controls, and data masking. Familiarity with cloud administration tools and frameworks such as AWS, dbt, Qlik Replicate, Tableau, and Snowflake is preferred. Job Title: Data Engineer Type: Contract Location: Miami FL | Dallas, Texas Company Overview The Data Engineer will provide technical leadership to the data platform engineering team by designing and implementing next‑generation data and analytics platforms and products using data engineering best practices. This role is hands‑on, contributing to engineering solutions while enabling business users through self‑service and automation. The Data Engineer is a key role in operationalizing the enterprise data fabric. Your Responsibilities on the Team Design, Build, and Operationalize: Formulate production‑grade data engineering solutions for enterprise data and analytics platforms and products. Pipeline Architecture: Architect and implement reliable ETL, ELT, and streaming data ingestion and delivery processes across multiple enterprise sources. Modern Python Development: Develop, maintain, and containerize modular data applications and utility scripts using Python on modern cloud infrastructure. Scale and Improve Infrastructure: Enhance data ingestion architecture with emphasis on data quality, cost‑performance, maintainability, and extensibility across storage and compute layers. Enforce Standards and Downstream Integrity: Define and implement engineering standards (code modularization, version control, automated testing, secure CI/CD workflows). Ensure strict schema evolution practices to protect downstream analytics. Platform Observability: Implement metrics, alerting, and monitoring (SLAs/SLOs) to ensure data platform reliability and trustworthiness. AI‑Driven Productivity: Use AI‑assisted development tools to accelerate coding, optimize queries, and improve delivery speed. AI/ML Integration: Collaborate with data science teams to design and optimize data layers supporting Generative AI, RAG, and LLM frameworks. Ecosystem Integration: Integrate data from diverse enterprise systems to enable analysts and data scientists to leverage optimized, end‑to‑end data products. Business Alignment: Develop a deep understanding of business processes and align data solutions with strategic objectives. Your Toolbox Bachelor’s degree in Computer Science, Information Technology, Management Information Systems, or a related field. 6+ years of experience in data engineering or a related role, with demonstrated success in delivering enterprise‑scale data solutions. Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning. Experience with cloud data warehouses like Snowflake, Databricks, and understanding of their appropriate use cases. Strong programming skills in Python or Java, with experience in data processing frameworks (e.g., Apache Spark, Hadoop). Experience with cloud platforms (AWS, Azure, GCP) and administration, such as AWS Redshift, Azure Synapse, or Google BigQuery. Proficiency with big data technologies, including Hadoop, Spark, Kafka, and HBase, with experience in distributed data processing. Expertise with data orchestration tools, such as MWAA/Airflow, for scheduling and managing data workflows. Experience with reporting tools such as Power BI and Tableau is a plus. Understanding of data security practices, including encryption, access controls, and data masking. Familiarity with cloud administration tools and frameworks such as AWS, dbt, Qlik Replicate, Tableau, and Snowflake is preferred. Core Expertise (8+ years preferred) Data Architecture & Enterprise Modeling: Advanced data warehousing, cloud data lakes, and multi‑layer architectures (Bronze, Silver, Gold). Advanced Data Transformations: Complex pipeline design, data cleansing, and standardization strategies. Production SDLC & Workflow Best Practices: Code reviews, testing/QA methodologies, and resilient error handling. Data Governance & Security: Role‑based access control (RBAC), compliance, and secure data environments. Strong Technical Experience (3–6+ years) Advanced Python Development: Production‑grade Python for data pipelines, automation, and APIs. AWS Platform & Containerization: ECS/ECR for containerized workloads Core services: S3, IAM, Lambda, EC2, CloudWatch, CloudTrail AWS certification is a plus Snowflake Data Cloud: Account administration and warehouse optimization Features: Data Sharing, Time Travel, Zero‑copy cloning dbt (Data Build Tool): Managing multi‑repo dbt environments Building and optimizing advanced models and macros AI‑Assisted Engineering & Data Tools: Experience with tools like GitHub Copilot, Cursor, or OpenAI APIs Familiarity with Snowflake Cortex, AWS Bedrock Exposure to LangChain, vector databases, embeddings Data Ingestion & Integration: Incremental loading, CDC methods Integration with complex REST APIs Version Control & CI/CD: Advanced Git workflows, PR enforcement, automated deployments Familiarity (1+ year) Orchestration tools such as Prefect, Airflow, or similar Data replication tools such as Qlik Replicate Soft Skills & Collaboration Product‑First Mindset: Ability to partner with stakeholders and translate business needs into technical solutions Collaborative Drive: Works effectively across engineering, DevOps, and data teams Technical Curiosity: Quickly learns new technologies and adapts to evolving business needs Equal Employment Opportunity Statement Gravity IT Resources is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected characteristic. All employment decisions are based on qualifications, merit, and business needs. #J-18808-Ljbffr