Senior Data Analytics Engineer

Key Responsibilities

The Senior Data Engineering & Analytics Specialist is responsible for designing, building, and optimizing scalable data platforms and analytical solutions that enable high-quality, data-driven decision-making. This role supports the full lifecycle of enterprise data assets—ranging from ingestion and transformation to modeling, analytics, and visualization—across hybrid cloud and on‑prem environments.

The ideal candidate combines strong data engineering expertise, deep SQL Server dashboarding, cloud knowledge, and practical analytics skills, enabling them to translate complex business requirements into reliable data pipelines, performant data models, and actionable insights.

• Design, build, and maintain robust, scalable ETL/ELT pipelines to ingest, transform, and load data from diverse sources, including IBM Netezza, and cloud-based platforms.
• Create and maintain optimal data pipeline architectures that support large-scale analytical workloads and evolving business needs.
• Implement incremental loads, change data capture (CDC), and data staging strategies to ensure process efficiency, reliability, and data integrity.
• Identify and implement process improvements, including automation, performance optimization, and infrastructure redesign for scalability.

• Configure and optimize SQL Server environments for high-throughput analytical use cases, including parallel query execution and indexing strategies.
• Design and implement partitioned tables, indexed views, and column store indexes to support large datasets and complex analytical queries.
• Manage and support SQL Server recovery models (Simple, Full, Bulk-Logged), including backup/restore strategies, log management, and disaster recovery planning.
• Support hybrid cloud and on‑prem data platforms, ensuring secure, efficient, and cost-effective data access.

• Design and maintain star and snowflake schemas, fact/dimension models, and slowly changing dimensions (SCDs).
• Translate business requirements into scalable, analytics-ready data models.
• Apply strong understanding of RDBMS, No
  
  SQL concepts, and data formats such as CSV, Parquet, and JSON.
• Partner with data governance and data strategy teams to improve data quality, consistency, and usability.

• Develop and maintain Power BI dashboards using DAX and M Code to deliver actionable insights into customer behavior, operational performance, and key business metrics.
• Apply data analytics techniques to identify trends, anomalies, and opportunities for optimization.
• Collaborate with stakeholders to understand analytical needs and support data-driven decision-making.

• Work closely with business partners, analysts, and technical teams to support data-related initiatives and resolve complex issues.
• Communicate technical concepts clearly to both technical and non-technical audiences.
• Exercise sound judgment to independently solve complex problems within established standards and governance frameworks.

• Strong programming skills in Python, SAS, and SQL.
• Experience with Power BI, including DAX and M Code.
• Proficiency with Microsoft 365 tools:
  Office, Power Automate, SharePoint, One Drive.

• Advanced SQL Server configuration for analytical workloads.
• Experience designing high-performance data structures (partitioning, indexing, column store).
• Strong understanding of backup, recovery, and disaster recovery strategies.
• Hands‑on experience building enterprise‑grade ETL pipelines for large datasets.

• Proven experience with dimensional modeling (star/snowflake, facts/dimensions, SCDs).
• Strong understanding of data integration, data warehousing, and enterprise data management.
• Ability to translate complex business requirements into scalable data solutions.

• Strong experience with AWS, including Redshift, Glue, and exposure to MLOps concepts.
• Familiarity with Apache Spark, Hadoop, and modern data lake architectures.

• Experience preparing and modeling data for…