Data Engineer/Analyst – Python, Spark, SQL, Cloud & Data Architecture

Job Summary Synechron is seeking a Lead – Data Engineer/Data Analyst to lead Data and Analytics projects and deliver data-driven solutions.

The role will combine technical expertise in data engineering, analytics, data architecture, modeling, integration, visualization, and governance with project delivery and team leadership responsibilities.

The successful candidate will work closely with clients and internal teams to understand business requirements, design effective data solutions, manage delivery plans, and ensure projects are completed on time, within budget, and to agreed quality standards.

The role will contribute to business value through reliable data, actionable insights, scalable architectures, and improved decision-making.

Software Requirements Required SQL: Strong experience with data analysis, data transformation, querying, validation, and database development.

Python: Strong experience in data engineering, analytics, automation, or data processing.

Apache Spark: Strong experience with distributed data processing and large-scale data workloads.

Database technologies: Experience with Oracle, MySQL, and SQL Server.

Data warehousing: Strong understanding of data warehouse architecture, modeling, and implementation.

Data lakes: Experience with data lake concepts, data ingestion, storage, processing, and governance.

Big data platforms: Strong technical understanding of platforms supporting large-scale data processing and analytics.

Data modeling: Experience designing conceptual, logical, and physical data models.

Data integration: Experience designing and implementing data integration solutions.

Data governance: Experience with data quality, standards, ownership, metadata, access, and lifecycle controls.

Data visualization and BI: Knowledge of Tableau and Power BI.

Cloud data solutions: Familiarity with AWS and Azure data environments.

Project delivery tools: Ability to use tools for project planning, delivery schedules, documentation, collaboration, and reporting.

Preferred Experience with cloud-native data architectures and data engineering services.

Experience delivering data-driven solutions in complex enterprise environments.

Experience with data quality automation, metadata management, lineage, and reconciliation.

Experience with advanced analytics, reporting, or self-service BI solutions.

Certification in data engineering, analytics, cloud, database, or BI technologies.

Experience improving data-processing efficiency and optimizing cloud resource usage.

Overall Responsibilities Lead and manage Data and Analytics projects from requirements definition through delivery and transition.

Work closely with clients and internal stakeholders to understand business requirements and develop data-driven solutions.

Provide technical expertise and guidance to junior team members and project teams.

Oversee data collection, cleansing, transformation, validation, and preparation processes.

Design and implement data architectures, data models, data warehouses, data lakes, and data integration solutions.

Develop and maintain project plans, delivery schedules, milestones, dependencies, risks, and resource plans.

Lead data analysis and modeling activities to address business and analytical requirements.

Guide the selection and application of data technologies, tools, platforms, and delivery approaches.

Ensure data solutions meet requirements for quality, scalability, maintainability, security, and performance.

Lead technical discussions with clients, architects, analysts, engineers, and other stakeholders.

Review technical outputs, data models, integration designs, analytical solutions, and project deliverables.

Monitor project progress, quality, budget, risks, and delivery timelines.

Mentor team members and support knowledge sharing, technical development, and consistent engineering practices.

Ensure project deliverables are completed on time, within budget, and in accordance with agreed standards.

Promote efficient use of data storage, processing, and cloud resources to support sustainable technology practices.

Technical Skills (By Category) Programming Languages Essential Python.

SQL.

Apache Spark development and data-processing capabilities.

Ability to write maintainable code for data transformation, validation, automation, and analytics.

Experience applying programming and query techniques to large-scale data workloads.

Preferred Experience with additional scripting or programming languages used for data engineering, automation, or analytics.

Experience developing reusable data-processing frameworks, utilities, or automation components.

Databases/Data Management Essential Oracle.

MySQL.

SQL Server.

Data warehousing.

Data lakes.

Big data platforms.

Data modeling and data integration.

Data collection, cleansing, preparation, transformation, and validation.

Data governance and data quality management.

Understanding of data structures, relationships, lineage, ownership, access, and lifecycle management.

Preferred Experience with data migration, reconciliation, metadata management, lineage, and master data practices.

Experience with database and data-platform performance optimization.

Experience designing data solutions for high-volume or complex analytical workloads.

Cloud Technologies Essential Familiarity with cloud-based data solutions.

AWS.

Azure.

Understanding of cloud data storage, processing, integration, scalability, availability, and governance considerations.

Preferred Experience designing or implementing cloud-native data architectures.

Experience with cloud data warehouses, data lakes, processing platforms, and integration services.

Experience with cloud monitoring, cost management, resource optimization, and sustainable data-processing practices.

Experience supporting cloud migration or data-platform modernization.

Frameworks and Libraries Essential Apache Spark.

Frameworks and libraries used for Python-based data engineering and analytics.

Components supporting data ingestion,