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Job Description & How to Apply Below
We are committed to innovation, sustainability, and delivering high-quality technology solutions that redefine efficiency in energy management and intelligent building operations.
ABOUT
THE ROLE
We are looking for a mid-to-senior Data Engineer to join LYNKED at a pivotal moment in our data evolution. This is a hands‑on role with real architecture ownership — you will build systems, not just data pipelines. You will take ownership of legacy data migration, ongoing data quality, and have real input on the platform architecture that underpins our smart metering and IoT systems working across workloads with constant flow of high‑volume telemetry from connected devices across the country.
Work on a Data Lakehouse foundation that supports both operational telemetry and analytical workloads. If you thrive in environments where you can build, clean, and improve systems end‑to‑end, and you are passionate about schema design and how to achieve pipeline reliability — this role was designed for you.
WHY JOIN US
Real impact: Shape the foundations of a modern Data Lakehouse architecture (Delta Lake on object storage) you will help shape how ingestion, storage, and serving layers scale in our data environments.
Build as you go: There are no simplistic checklists here. You will define the right standards and structure where it is needed and continuously improve what is not working.
High visibility: Your work will not be buried — it will directly influence key systems, decisions, and future growth.
Own your work: From legacy migration to ingestion to transformation to serving — you will see systems end‑to‑end and take pride in what you build.
WHAT YOU'LL DO
Design and evolve the Lakehouse storage architecture (Delta Lake) across raw, curated, and serving layers.
Lead legacy data migration efforts: assess source systems, profile data quality, design source‑to‑target mappings, build cleansing and reconciliation logic, and load historical data into the modern Lakehouse with lineage and audit trails.
Establish data quality standards:
Define and enforce data validation rules, schema contracts, validation checks, anomaly detection, and remediation workflows.
Design, build, and optimize ETL/ELT data ingestion pipelines across multiple systems.
Ensure pipelines run reliably in production through observability (logging, metrics, lineage), failure handling, and performance tuning.
Work with modern file formats (Parquet, Delta, Iceberg), applying them appropriately for performance, cost, and schema evolution.
Implement data security and governance: role‑based access, PII handling, encryption at rest and in transit, audit logging, and adherence to applicable privacy frameworks.
Collaborate with engineering and product teams to build scalable, maintainable data solutions.
Contribute to improving data architecture and overall system reliability.
WHAT WE'RE LOOKING FOR
3+ years of experience in a Data Engineering or equivalent hands‑on data role.
Strong experience designing and developing data systems and infrastructure. Comfortable navigating evolving systems, building from scratch, and filling gaps where needed.
Proven experience contributing significantly to data migration efforts: assessing source systems, mapping, cleansing, reconciliation, and cutover.
Strong knowledge of data modeling: solid grasp of OLTP vs OLAP design, normalization vs denormalization, dimensional modeling (Kimball), and slowly changing dimensions.
Expert proficient in SQL, with hands‑on experience with relational systems like Postgre
SQL, Oracle, or equivalents.
Hands‑on with data…
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