Data Engineer designing and optimizing data pipelines using Databricks and Google Cloud Platform. Collaborating with analysts and scientists to deliver high - quality data products.
About the role
- Senior Data Engineer at Lightcast responsible for building data infrastructure. Collaborate with engineering and analytics teams to support machine learning and enterprise decision-making.
Responsibilities
- Architect, build, and maintain scalable, reliable data pipelines (batch & streaming) to ingest, transform, and deliver data for analytics, reporting, and ML use cases.
- Architect software applications, test, and build automated tools.
- Translate complex functional and technical requirements into architecture designs and high-performing software solutions.
- Select appropriate data solution software and define hardware requirements to support performance and scalability.
- Develop and implement standards and processes for data integration projects and initiatives.
- Lead the design and development of software applications, testing, and building tools.
- Optimize SQL queries (joins, window functions, aggregations, partitioning, indexing) and data schema performance.
- Design data models, schemas, and data warehouses/data lakes (dimensional, star, snowflake schemas, normalization/denormalization).
- Ensure data quality, correctness, and consistency across datasets (validation, anomaly detection, reconciliation).
- Ensure database changes are reviewed and approved according to standards.
- Monitor, troubleshoot, and tune performance of pipelines, databases, and workloads.
- Drive adoption of engineering best practices: version control, CI/CD, testing (unit and integration for data pipelines), documentation, and code reviews.
- Collaborate with software engineers to integrate data systems into production environments.
- Provide technical assistance to junior members and to colleagues across the company.
- Mentor and coach junior and mid-level engineers, promoting engineering discipline across the team.
- Evaluate and propose new tools, frameworks, and technologies for the data platform.
- Ensure data security, governance, access control, lineage, and compliance (e.g., GDPR, CCPA, internal standards).
Requirements
- Bachelor’s degree in Computer Science or a related technical discipline (Master’s preferred)
- 5+ years of professional experience in data engineering, software engineering, or data science
- Expert-level SQL, including query optimization, advanced joins, windowing, partitioning, and indexing
- Proven expertise in Snowflake for data warehousing and advanced analytics
- Strong background in data modeling, data engineering best practices, and distributed systems (e.g., Spark, Hadoop, Hive, Presto)
- Hands-on experience designing and maintaining ETL/ELT pipelines, data integration (APIs, event streams, logs), and workflow orchestration (Airflow or Astronomer required)
- Proficiency with modern data stack tools, including DBT for transformation and modeling
- Experience with AWS cloud services for data engineering and infrastructure management
- Strong software engineering skills, including clean code practices, modularization, error handling, logging, CI/CD, and automated testing
- Knowledge of object-oriented design, data structures, algorithms, and disaster recovery strategies for data systems
- Skilled in scalability and performance optimization across pipelines, databases, and workloads
- Familiarity with Python (preferred) and other modern programming languages
- Demonstrated ability to analyze complex data sets, identify trends, and derive actionable insights
- Effective collaborator with cross-functional teams and proven success in mentoring junior engineers
- Experience with agile or rapid application development methodologies
- Highly creative, detail-oriented, and results-driven with strong problem-solving and prioritization skills
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