Data Engineer

Data Engineer Overview We are seeking a Data Engineer to support large‑scale data initiatives, build modern data pipelines, and help transform legacy data systems into scalable, cloud‑based platforms. The ideal candidate has strong experience with ETL/ELT development, cloud technologies, big‑data processing, and enterprise data models. This role partners closely with architects, product teams, and business stakeholders to deliver high‑quality, governed, and reliable data solutions. Responsibilities • Design, build, and maintain scalable data pipelines to ingest, transform, and deliver data across multiple sources and environments. • Migrate data from legacy/on‑prem systems to modern cloud data platforms. • Develop ETL/ELT workflows using tools such as Databricks, Spark, Glue, Airflow, Dataflow, or similar technologies. • Build and optimize data models to support analytics, reporting, and application use cases. • Work with structured, semi‑structured, and unstructured data (CSV, JSON, Parquet, APIs, streaming data). • Collaborate with data architects and engineers to implement best practices in data architecture, quality, governance, and security. • Troubleshoot and optimize data pipelines for performance, reliability, and cost. • Implement data quality checks, monitoring, and alerting to ensure trust and consistency across environments. • Support CI/CD pipelines for data engineering workflows and automate deployment processes. • Participate in Agile ceremonies and work closely with product owners, analysts, and business partners. Required Qualifications • 3+ years of professional experience in data engineering or software engineering with strong data work. • Hands‑on experience with ETL/ELT pipelines, big‑data processing frameworks, and data modeling. • Strong proficiency in SQL and one programming language (Python, Java, or Scala). • Experience working with at least one major cloud platform (AWS, Azure, or GCP). • Familiarity with data warehousing concepts, distributed systems, and pipeline orchestration tools. • Experience with version control tools (Git) and CI/CD pipelines. • Strong understanding of data quality, lineage, metadata, and governance. • Ability to troubleshoot complex data issues and work in a fast-paced, collaborative environment. Preferred Qualifications • Experience with Databricks, Spark, Snowflake, BigQuery, Redshift, or Synapse. • Background in large-scale data migrations or modernizing legacy systems. • Experience with streaming technologies (Kafka, Pub/Sub, Kinesis, EventHub). • Exposure to MDM, data cataloging, and enterprise governance frameworks. • Experience in highly regulated industries (automotive, finance, healthcare, etc.). • Familiarity with containerization tools (Docker, Kubernetes).